As an important branch of machine learning,clustering analysis is widely used in some fields,e.g.,image pattern recognition,social network analysis,information security,and so on.In this paper,we consider the designin...As an important branch of machine learning,clustering analysis is widely used in some fields,e.g.,image pattern recognition,social network analysis,information security,and so on.In this paper,we consider the designing of clustering algorithm in quantum scenario,and propose a quantum hierarchical agglomerative clustering algorithm,which is based on one dimension discrete quantum walk with single-point phase defects.In the proposed algorithm,two nonclassical characters of this kind of quantum walk,localization and ballistic effects,are exploited.At first,each data point is viewed as a particle and performed this kind of quantum walk with a parameter,which is determined by its neighbors.After that,the particles are measured in a calculation basis.In terms of the measurement result,every attribute value of the corresponding data point is modified appropriately.In this way,each data point interacts with its neighbors and moves toward a certain center point.At last,this process is repeated several times until similar data points cluster together and form distinct classes.Simulation experiments on the synthetic and real world data demonstrate the effectiveness of the presented algorithm.Compared with some classical algorithms,the proposed algorithm achieves better clustering results.Moreover,combining quantum cluster assignment method,the presented algorithm can speed up the calculating velocity.展开更多
Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketin...Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.展开更多
Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an...Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.展开更多
This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the ...This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the most recent study(2019-2020)identified 56 individuals with the time between encounters from one to 344 days between the first and last sighting.Site Fidelity(SF)and Residence(RES)indices were calculated and Agglomerative Hierarchical Clustering(AHC)modeling was performed,with three patterns of residence obtained:resident(25%),semi-resident(17.86%)and transient(57.14%).These results were contrasted with remodeled data from a previous study(2006-2007),showing similar patterns:resident(24.44%),semi-resident(28.89%)and transient(46.67%).Importantly,two individuals were found to have been resident over the extended period.A breeding female sighted for the first time in 2004 and again in 2020(16 years)and the other from 2005 to 2020(15 years).This region is an important area for marine mammals,known to support a resident reproductive population over many years,as well seabirds,sea turtles,whale sharks and fishermen.We recommend that consideration be given to designating the waters as a Marine Protected Area to safeguard the existing population and provide benefit to the surrounding marine environment.展开更多
基金This work was supported by National Natural Science Foundation of China(Grants Nos.61976053 and 61772134)Fujian Province Natural Science Foundation(Grant No.2018J01776)+1 种基金Program for New Century Excellent Talents in Fujian Province University,Probability and Statistics:Theory and Application(Grant No.IRTL1704)the Program for Innovative Research Team in Science and Technology in Fujian Province University.
文摘As an important branch of machine learning,clustering analysis is widely used in some fields,e.g.,image pattern recognition,social network analysis,information security,and so on.In this paper,we consider the designing of clustering algorithm in quantum scenario,and propose a quantum hierarchical agglomerative clustering algorithm,which is based on one dimension discrete quantum walk with single-point phase defects.In the proposed algorithm,two nonclassical characters of this kind of quantum walk,localization and ballistic effects,are exploited.At first,each data point is viewed as a particle and performed this kind of quantum walk with a parameter,which is determined by its neighbors.After that,the particles are measured in a calculation basis.In terms of the measurement result,every attribute value of the corresponding data point is modified appropriately.In this way,each data point interacts with its neighbors and moves toward a certain center point.At last,this process is repeated several times until similar data points cluster together and form distinct classes.Simulation experiments on the synthetic and real world data demonstrate the effectiveness of the presented algorithm.Compared with some classical algorithms,the proposed algorithm achieves better clustering results.Moreover,combining quantum cluster assignment method,the presented algorithm can speed up the calculating velocity.
文摘Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.
基金funded by Huanggang Normal University,China,Self-type Project of 2021(No.30120210103)and 2022(No.2042021008).
文摘Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.
基金We thank the fisherman José“Cata”,Grisel Velásquez(UNISIG-IVIC),Laboratory of Ecosystems and Global Change,Venezuelan Institute of Scientific Research,PADI Foundation(N°40470)the Cetacean Society International and the Society of Marine Mammalogy for their funding which enabled this study.
文摘This study represents the first comprehensive analysis of the residency patterns of a coastal population of bottlenose dolphin off the coast of Aragua,Venezuela,over a multi-year period.Using photo-identification,the most recent study(2019-2020)identified 56 individuals with the time between encounters from one to 344 days between the first and last sighting.Site Fidelity(SF)and Residence(RES)indices were calculated and Agglomerative Hierarchical Clustering(AHC)modeling was performed,with three patterns of residence obtained:resident(25%),semi-resident(17.86%)and transient(57.14%).These results were contrasted with remodeled data from a previous study(2006-2007),showing similar patterns:resident(24.44%),semi-resident(28.89%)and transient(46.67%).Importantly,two individuals were found to have been resident over the extended period.A breeding female sighted for the first time in 2004 and again in 2020(16 years)and the other from 2005 to 2020(15 years).This region is an important area for marine mammals,known to support a resident reproductive population over many years,as well seabirds,sea turtles,whale sharks and fishermen.We recommend that consideration be given to designating the waters as a Marine Protected Area to safeguard the existing population and provide benefit to the surrounding marine environment.
