A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared...A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.展开更多
A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input d...A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.展开更多
The information centric network(ICN)has been widely discussed in current researches.The ICN interoperation with a traditional IP network and caching methods are one of the research topics of interest.For economic reas...The information centric network(ICN)has been widely discussed in current researches.The ICN interoperation with a traditional IP network and caching methods are one of the research topics of interest.For economic reasons,the capability of applying the ICN to internet service providers(ISPs)with various traditional IP protocols already implemented,especially IGP,MPLS,VRF,and TE,does not require any change on the IP network infrastructure.The biggest concern of ISPs is related to their customers’contents delivery speed.In this paper,we consider ICN caching locations in ISP by using the concept of locator/ID separation protocol(LISP)for interoperation between a traditional IP address and name-based ICN.To be more specific,we propose a new procedure to determine caching locations in the ICN by using the cuckoo search algorithm(CSA)for finding the best caching locations of information chunks.Moreover,we create the smart control plane(SCP)scheme which is an intelligent controlling,managing,and mapping system.Its function is similar to the software defined network concept.We show how the proposed SCP system works in both synthetic small network and real-world big network.Finally,we show and evaluate the performance of our algorithm comparison with the simple search method using the shortest path first algorithm.展开更多
基金supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the PhD Program Thai Doctoral degree
文摘A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.
基金supported by Chiang Mai University Research Fund under the contract number T-M5744
文摘A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.
文摘The information centric network(ICN)has been widely discussed in current researches.The ICN interoperation with a traditional IP network and caching methods are one of the research topics of interest.For economic reasons,the capability of applying the ICN to internet service providers(ISPs)with various traditional IP protocols already implemented,especially IGP,MPLS,VRF,and TE,does not require any change on the IP network infrastructure.The biggest concern of ISPs is related to their customers’contents delivery speed.In this paper,we consider ICN caching locations in ISP by using the concept of locator/ID separation protocol(LISP)for interoperation between a traditional IP address and name-based ICN.To be more specific,we propose a new procedure to determine caching locations in the ICN by using the cuckoo search algorithm(CSA)for finding the best caching locations of information chunks.Moreover,we create the smart control plane(SCP)scheme which is an intelligent controlling,managing,and mapping system.Its function is similar to the software defined network concept.We show how the proposed SCP system works in both synthetic small network and real-world big network.Finally,we show and evaluate the performance of our algorithm comparison with the simple search method using the shortest path first algorithm.
