The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside...The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.展开更多
Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts.The concept of data publishing faces a lot of security issues,indicating that when any trusted...Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts.The concept of data publishing faces a lot of security issues,indicating that when any trusted organization provides data to a third party,personal information need not be disclosed.Therefore,to maintain the privacy of the data,this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer(Genetic GWO)algorithm for which a C-mixture parameter is used.The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints,such as the k-anonymity,l-diversity and the m-privacy.A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size.The minimum value of the fitness ensures the maximum utility and the maximum privacy.Experimentation was carried out using the adult dataset,and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9,respectively.展开更多
基金supported by proposal No.OSD/BCUD/392/197 Board of Colleges and University Development,Savitribai Phule Pune University,Pune
文摘The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers.
文摘Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts.The concept of data publishing faces a lot of security issues,indicating that when any trusted organization provides data to a third party,personal information need not be disclosed.Therefore,to maintain the privacy of the data,this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer(Genetic GWO)algorithm for which a C-mixture parameter is used.The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints,such as the k-anonymity,l-diversity and the m-privacy.A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size.The minimum value of the fitness ensures the maximum utility and the maximum privacy.Experimentation was carried out using the adult dataset,and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9,respectively.
