This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfac...This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.展开更多
BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available bi...BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.展开更多
Objective:Using data mining technology to explore the rules of traditional Chinese medicine(TCM)in the treatment of threatened abortion in the early stage of pregnancy with sub-chorionic haematoma(SCH).Methods:Literat...Objective:Using data mining technology to explore the rules of traditional Chinese medicine(TCM)in the treatment of threatened abortion in the early stage of pregnancy with sub-chorionic haematoma(SCH).Methods:Literature of TCM in the treatment of threatened abortion in the early stage of pregnancy with SCH were retrieved from CNKI,VIP,WANFANG and Pubmed,EMBASE.The literature information database was established to be used for descriptive analysis,association rule analysis and cluster analysis of relevant data.Results:A total of 100 literatures were included,involving 114 Chinese herbs.The efficacy of Chinese herbs were mainly tonic drugs,hemostatic drugs,heat-clearing drugs,dissolving blood stasis and hemostatic drugs.The medicinal properties were mostly mild and warm,and the taste of the drug was mainly sweet,bitter and pungent.The liver meridian,spleen meridian and kidney meridian were frequently used.The commonly used drug pair combination was"Xu duan(Radix dipsaci,续断)-Tusizi(Semen Cuscutae,菟丝子)",and the core combination was"Tusizi-Xu duan-Ejiao(Donkeyhide gelatin,阿胶)-Baizhu(Atractylodes macrocephala,白术)-Dangshen(Codonopsis pilosula,党参)".Commonly used drugs for removing blood stasis and hemostasis were with Sanqi(Panax notoginseng,三七),Puhuang(cattail pollen,蒲黄),and Qiancao(Radix Rubiae,茜草).Conclusion:Data mining traditional Chinese medicine for the treatment of threatened abortion in the early stage of pregnancy with SCH clinically commonly used drug efficacy,taste,meridian,commonly used drug pairs,core combination and commonly used blood stasis hemostatic drugs,has important reference significance for the treatment of threatened abortion in the early stage of pregnancy combined with SCH.展开更多
For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this p...For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this paper,we propose a new approach,named SVR-Miner(Security Validation Rules Miner),which uses frequent sequence mining technique [1-4] to automatically infer implicit security validation rules from large software code written in C programming language.Different from the past works in this area,SVR-Miner introduces three techniques which are sensitive thread,program slicing [5-7],and equivalent statements computing to improve the accuracy of rules.Experiments with the Linux Kernel demonstrate the effectiveness of our approach.With the ten given sensitive threads,SVR-Miner automatically generated 17 security validation rules and detected 8 violations,5 of which were published by Linux Kernel Organization before we detected them.We have reported the other three to the Linux Kernel Organization recently.展开更多
An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic rela...An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.展开更多
By the microseismic (MS) monitoring system of Sanhejian Coal Mine,the detail MS activity rules in the entire mining process of 9202 strong rockburst working face were studied,following main conclusions were obtained.(...By the microseismic (MS) monitoring system of Sanhejian Coal Mine,the detail MS activity rules in the entire mining process of 9202 strong rockburst working face were studied,following main conclusions were obtained.(1) The strong correlation between MS activity and the region stress gradient was revealed.The higher the region stress gradient, the stronger the MS signal is,and the frequency-spectrum moves to lower frequency band the amplitude begins to add gradually.(2) The different types of MS signals have the cor- responding frequency-spectrum character.Such as relieve-shot MS signal shows the wide frequency-spectrum,multi-peak high frequency character,while rockburst omen signal shows the low frequency and amplitude,the mainshock signal has relatively higher fre- quency and amplitude.(3) To monitor and recognize rockburst dangerous region,the strong consistence between the MS signal intensity and the amplitude of electromagnetic emission (EME) signal and drilling bits measured was observed.On above,the weakening and controlling technology of MS intensity was put forward.展开更多
Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results conta...Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.展开更多
The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates ...The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.展开更多
Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting corre...Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.展开更多
Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at...Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.展开更多
The Apriori algorithm is a classical method of association rules mining.Based on analysis of this theory,the paper provides an improved Apriori algorithm.The paper puts foward with algorithm combines HASH table techni...The Apriori algorithm is a classical method of association rules mining.Based on analysis of this theory,the paper provides an improved Apriori algorithm.The paper puts foward with algorithm combines HASH table technique and reduction of candidate item sets to enhance the usage efficiency of resources as well as the individualized service of the data library.展开更多
Exceptional rules are often ignored because of their small support. However, they have high confidence, so they are useful sometimes. A new algorithm for mining exceptional rules is presented, which creates a large it...Exceptional rules are often ignored because of their small support. However, they have high confidence, so they are useful sometimes. A new algorithm for mining exceptional rules is presented, which creates a large itemset from a relatively small database and scans the whole database only one time to generate all exceptional rules. This algorithm is proved to be quick and effective through its application in a mushroom database.展开更多
Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider neg...Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items （i. e. absent from transactions）. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP tree is presented to discover negative association rules.展开更多
Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain a...Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which containeddata on physical environment (land cover, river, road and city center), socio-economic (income source, population, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experiment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and other factors.The association among hotspots occurrence with other geographical objects was discovered for the minimum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These factors are precipitation greater than or equal to 3 mm/day, wind speed in [1m/s, 2m/s), non peatland area, screen temperature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to the nearest road less than or equal to 2.5 km.展开更多
Mining association rules from large database is very costly. We develop a parallel algorithm for this task on shared-memory multiprocessor (SMP). Most proposed parallel algorithms for association rules mining have to ...Mining association rules from large database is very costly. We develop a parallel algorithm for this task on shared-memory multiprocessor (SMP). Most proposed parallel algorithms for association rules mining have to scan the database at least two times. In this article, a parallel algorithm Scan Once (SO) has been proposed for SMP, which only scans the database once. And this algorithm is fundamentally different from the known parallel algorithm Count Distribution (CD). It adopts bit matrix to store the database information and gets the support of the frequent itemsets by adopting Vector-And-Operation, which greatly improve the efficiency of generating all frequent itemsets. Empirical evaluation shows that the algorithm outperforms the known one CD algorithm.展开更多
Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.Th...Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.展开更多
In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers...In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers not only adding new data into the database but also reducing old data from the database. Furthermore, it can predigest five cases to three cases.The algorithm proposed in this letter can avoid generating lots of candidate items, and it is high efficient.展开更多
Objective:To study the prescription and medication rule of Professor Hua Baojin in the treatment of colorectal cancer through data mining,so as to provide reference for clinical treatment of colorectal cancer. Methods...Objective:To study the prescription and medication rule of Professor Hua Baojin in the treatment of colorectal cancer through data mining,so as to provide reference for clinical treatment of colorectal cancer. Methods:The outpatient medical records of Professor Hua Baojin from June 2015 to October 2020 in Guang’anmen Hospital,China Academy of Chinese Medical Sciences were collected. TCM Inheritance Support Platform(V2.5)was used to analyze high-frequency drugs,drug properties,flavors,channel tropism,common drug combinations,core combinations and new prescriptions. Results:A total of 500 prescriptions were included,involving 222 traditional Chinese medicines and 38 high-frequency(≥100)medicines,including Atractylodes,Poria cocos,ginger,etc. The most common medicinal properties of drugs were warm,cold and mild,and flavors were sweet,bitter and pungent,channel tropism included spleen,stomach,liver and lung meridians. 36 groups of common drug combinations and 20 association rules were obtained by data mining,and 18 core combinations and 9 new prescriptions were evolved.Conclusion:Professor Hua Baojin takes recuperating spleen and stomach as the core in the treatment of colorectal cancer,attaching importance to regulating the rise and fall of qi,adding and subtracting on the basis of Xiangsha Liujunzi Decoction,flexibly selecting the drugs of dispelling blood stasis,resolving phlegm,detoxification and loose knots,and using both cold and warm in the prescription,tonifying and reducing at the same time.展开更多
Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques w...Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.展开更多
Objective:To analyze the clinical research literatures of TCM syndromes and treatment of Immunoglobulin A Nephropathy(IgAN)on data-mining technology,and explore the rules of TCM syndromes and Chinese medication.Method...Objective:To analyze the clinical research literatures of TCM syndromes and treatment of Immunoglobulin A Nephropathy(IgAN)on data-mining technology,and explore the rules of TCM syndromes and Chinese medication.Methods:By searching the clinical literatures about TCM syndromes and treatment of IgAN had published in China Biomedical Literature Database,CNKI Database,Wanfang Database,and Chongqing Weipu Database from January 2000 to September 2019.Strictly according to the inclusion and exclusion criteria,included documents and established the databases.Applicated of statistical software for frequency analysis of TCM syndromes,Chinese herbal medicines,and analyzed the commonly used Chinese herbal medicines by factor analysis and cluster analysis.Results:292 literatures were finally included,involving 28 syndromes.A total of 479 prescriptions and 254 Chinese herbs were used.The rules of syndromes in IgA nephropathy are as follows:the syndromes are mainly composed of Qi and Yin deficiency syndrome,followed by the Spleen and kidney qi deficiency syndrome,Liver and Kidney Yin Deficiency Syndrome,Spleen and kidney yang deficiency syndrome.The blood-stasis syndrome,damp-heat syndrome and wind-heat disturbance syndrome are accompanied by other syndrome.The characteristics of traditional Chinese medicines for IgA nephropathy is mainly composed of tonic deficiency medicines,supplemented by blood-activating and stasis-eliminating medicines,heat-clearing medicines,inducing diuresis and excreting dampness medicines and astringent medicines to combine with formulas.Conclusion:Through Data-mining method systematically summarized the rules of TCM syndromes and Chinese medication in treating IgAN and provided scientific theoretical guidance for the treatment of IgAN.展开更多
文摘This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.
文摘BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.
基金Clinical observation and metabolomics study of patients with Phlegm-stasis interjunction polycystic ovary syndrome by Guangdong Bureau of Traditional Chinese Medicine (20202066)Shenzhen Baoan district science and technology plan (20200505115910988)Observation on the efficacy of Jiaxiao Dingjing Decoction combined with clomiphene in the treatment of polycystic ovary syndrome (2020JD526)。
文摘Objective:Using data mining technology to explore the rules of traditional Chinese medicine(TCM)in the treatment of threatened abortion in the early stage of pregnancy with sub-chorionic haematoma(SCH).Methods:Literature of TCM in the treatment of threatened abortion in the early stage of pregnancy with SCH were retrieved from CNKI,VIP,WANFANG and Pubmed,EMBASE.The literature information database was established to be used for descriptive analysis,association rule analysis and cluster analysis of relevant data.Results:A total of 100 literatures were included,involving 114 Chinese herbs.The efficacy of Chinese herbs were mainly tonic drugs,hemostatic drugs,heat-clearing drugs,dissolving blood stasis and hemostatic drugs.The medicinal properties were mostly mild and warm,and the taste of the drug was mainly sweet,bitter and pungent.The liver meridian,spleen meridian and kidney meridian were frequently used.The commonly used drug pair combination was"Xu duan(Radix dipsaci,续断)-Tusizi(Semen Cuscutae,菟丝子)",and the core combination was"Tusizi-Xu duan-Ejiao(Donkeyhide gelatin,阿胶)-Baizhu(Atractylodes macrocephala,白术)-Dangshen(Codonopsis pilosula,党参)".Commonly used drugs for removing blood stasis and hemostasis were with Sanqi(Panax notoginseng,三七),Puhuang(cattail pollen,蒲黄),and Qiancao(Radix Rubiae,茜草).Conclusion:Data mining traditional Chinese medicine for the treatment of threatened abortion in the early stage of pregnancy with SCH clinically commonly used drug efficacy,taste,meridian,commonly used drug pairs,core combination and commonly used blood stasis hemostatic drugs,has important reference significance for the treatment of threatened abortion in the early stage of pregnancy combined with SCH.
基金National Natural Science Foundation of China under Grant No.60873213,91018008 and 61070192Beijing Science Foundation under Grant No. 4082018Shanghai Key Laboratory of Intelligent Information Processing of China under Grant No. IIPL-09-006
文摘For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this paper,we propose a new approach,named SVR-Miner(Security Validation Rules Miner),which uses frequent sequence mining technique [1-4] to automatically infer implicit security validation rules from large software code written in C programming language.Different from the past works in this area,SVR-Miner introduces three techniques which are sensitive thread,program slicing [5-7],and equivalent statements computing to improve the accuracy of rules.Experiments with the Linux Kernel demonstrate the effectiveness of our approach.With the ten given sensitive threads,SVR-Miner automatically generated 17 security validation rules and detected 8 violations,5 of which were published by Linux Kernel Organization before we detected them.We have reported the other three to the Linux Kernel Organization recently.
基金The National Natural Science Foundation of China(No.50674086)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060290508)the Science and Technology Fund of China University of Mining and Technology(No.2007B016)
文摘An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.
基金the National Key Project of Scientific and Technical Supporting Programs(2006BAK04B02,2006BAK03B06)
文摘By the microseismic (MS) monitoring system of Sanhejian Coal Mine,the detail MS activity rules in the entire mining process of 9202 strong rockburst working face were studied,following main conclusions were obtained.(1) The strong correlation between MS activity and the region stress gradient was revealed.The higher the region stress gradient, the stronger the MS signal is,and the frequency-spectrum moves to lower frequency band the amplitude begins to add gradually.(2) The different types of MS signals have the cor- responding frequency-spectrum character.Such as relieve-shot MS signal shows the wide frequency-spectrum,multi-peak high frequency character,while rockburst omen signal shows the low frequency and amplitude,the mainshock signal has relatively higher fre- quency and amplitude.(3) To monitor and recognize rockburst dangerous region,the strong consistence between the MS signal intensity and the amplitude of electromagnetic emission (EME) signal and drilling bits measured was observed.On above,the weakening and controlling technology of MS intensity was put forward.
基金Under the auspices of Special Fund of Ministry of Land and Resources of China in Public Interest(No.201511001)
文摘Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.
文摘The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.
文摘Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.
文摘Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.
文摘The Apriori algorithm is a classical method of association rules mining.Based on analysis of this theory,the paper provides an improved Apriori algorithm.The paper puts foward with algorithm combines HASH table technique and reduction of candidate item sets to enhance the usage efficiency of resources as well as the individualized service of the data library.
基金This project was supported by the National Natural Science Foundation of China (No. 69835010).
文摘Exceptional rules are often ignored because of their small support. However, they have high confidence, so they are useful sometimes. A new algorithm for mining exceptional rules is presented, which creates a large itemset from a relatively small database and scans the whole database only one time to generate all exceptional rules. This algorithm is proved to be quick and effective through its application in a mushroom database.
基金Supported by the National Natural Science Foun-dation of China(70371015) and the Science Foundation of JiangsuUniversity ( 04KJD001)
文摘Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items （i. e. absent from transactions）. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP tree is presented to discover negative association rules.
文摘Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which containeddata on physical environment (land cover, river, road and city center), socio-economic (income source, population, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experiment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and other factors.The association among hotspots occurrence with other geographical objects was discovered for the minimum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These factors are precipitation greater than or equal to 3 mm/day, wind speed in [1m/s, 2m/s), non peatland area, screen temperature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to the nearest road less than or equal to 2.5 km.
文摘Mining association rules from large database is very costly. We develop a parallel algorithm for this task on shared-memory multiprocessor (SMP). Most proposed parallel algorithms for association rules mining have to scan the database at least two times. In this article, a parallel algorithm Scan Once (SO) has been proposed for SMP, which only scans the database once. And this algorithm is fundamentally different from the known parallel algorithm Count Distribution (CD). It adopts bit matrix to store the database information and gets the support of the frequent itemsets by adopting Vector-And-Operation, which greatly improve the efficiency of generating all frequent itemsets. Empirical evaluation shows that the algorithm outperforms the known one CD algorithm.
基金supported by the National Natural Science Foundation of China(Grant No.82104701)Science Fund Program for Outstanding Young Scholars in Universities of Anhui Province(Grant No.2022AH030064)+3 种基金Key Project at Central Government Level:the Ability Establishment of Sustainable Use for Valuable Chinese Medicine Resources(Grant No.2060302)Foundation of Anhui Province Key Laboratory of Pharmaceutical Preparation Technology and Application(Grant No.2021KFKT10)China Agriculture Research System of MOF and MARA(Grant No.CARS-21)Talent Support Program of Anhui University of Chinese Medicine(Grant No.2020rcyb007).
文摘Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.
基金Supported in part by the National Natural Science Foundation of China(No.60073012),Natural Science Foundation of Jiangsu(BK2001004)
文摘In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers not only adding new data into the database but also reducing old data from the database. Furthermore, it can predigest five cases to three cases.The algorithm proposed in this letter can avoid generating lots of candidate items, and it is high efficient.
基金National Natural Science Foundation of China(No.81673961,81774294)Beijing Natural Science Foundation(No.7172186)。
文摘Objective:To study the prescription and medication rule of Professor Hua Baojin in the treatment of colorectal cancer through data mining,so as to provide reference for clinical treatment of colorectal cancer. Methods:The outpatient medical records of Professor Hua Baojin from June 2015 to October 2020 in Guang’anmen Hospital,China Academy of Chinese Medical Sciences were collected. TCM Inheritance Support Platform(V2.5)was used to analyze high-frequency drugs,drug properties,flavors,channel tropism,common drug combinations,core combinations and new prescriptions. Results:A total of 500 prescriptions were included,involving 222 traditional Chinese medicines and 38 high-frequency(≥100)medicines,including Atractylodes,Poria cocos,ginger,etc. The most common medicinal properties of drugs were warm,cold and mild,and flavors were sweet,bitter and pungent,channel tropism included spleen,stomach,liver and lung meridians. 36 groups of common drug combinations and 20 association rules were obtained by data mining,and 18 core combinations and 9 new prescriptions were evolved.Conclusion:Professor Hua Baojin takes recuperating spleen and stomach as the core in the treatment of colorectal cancer,attaching importance to regulating the rise and fall of qi,adding and subtracting on the basis of Xiangsha Liujunzi Decoction,flexibly selecting the drugs of dispelling blood stasis,resolving phlegm,detoxification and loose knots,and using both cold and warm in the prescription,tonifying and reducing at the same time.
文摘Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discovered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms.
基金National Natural Science Foundation of China(No.81760807)Guangxi University of Traditional Chinese Medicine 2019 Graduate Education Innovation Program Project(No.YCSY20190066)。
文摘Objective:To analyze the clinical research literatures of TCM syndromes and treatment of Immunoglobulin A Nephropathy(IgAN)on data-mining technology,and explore the rules of TCM syndromes and Chinese medication.Methods:By searching the clinical literatures about TCM syndromes and treatment of IgAN had published in China Biomedical Literature Database,CNKI Database,Wanfang Database,and Chongqing Weipu Database from January 2000 to September 2019.Strictly according to the inclusion and exclusion criteria,included documents and established the databases.Applicated of statistical software for frequency analysis of TCM syndromes,Chinese herbal medicines,and analyzed the commonly used Chinese herbal medicines by factor analysis and cluster analysis.Results:292 literatures were finally included,involving 28 syndromes.A total of 479 prescriptions and 254 Chinese herbs were used.The rules of syndromes in IgA nephropathy are as follows:the syndromes are mainly composed of Qi and Yin deficiency syndrome,followed by the Spleen and kidney qi deficiency syndrome,Liver and Kidney Yin Deficiency Syndrome,Spleen and kidney yang deficiency syndrome.The blood-stasis syndrome,damp-heat syndrome and wind-heat disturbance syndrome are accompanied by other syndrome.The characteristics of traditional Chinese medicines for IgA nephropathy is mainly composed of tonic deficiency medicines,supplemented by blood-activating and stasis-eliminating medicines,heat-clearing medicines,inducing diuresis and excreting dampness medicines and astringent medicines to combine with formulas.Conclusion:Through Data-mining method systematically summarized the rules of TCM syndromes and Chinese medication in treating IgAN and provided scientific theoretical guidance for the treatment of IgAN.
