The authors discuss the concept of meta information which is the description of information system or its subsystems, and proposes algorithms for meta information generation. Meta information can be generated in pa...The authors discuss the concept of meta information which is the description of information system or its subsystems, and proposes algorithms for meta information generation. Meta information can be generated in parallel mode and network computation can be used to accelerate meta information generation. Most existing rough set methods assume information system to be centralized and cannot be applied directly in distributed information system. Data integration, which is costly, is necessary for such existing methods. However, meta information integration will eliminate the need of data integration in many cases, since many rough set operations can be done straightforward based on meta information, and many existing methods can be modified based on meta information.展开更多
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
文摘The authors discuss the concept of meta information which is the description of information system or its subsystems, and proposes algorithms for meta information generation. Meta information can be generated in parallel mode and network computation can be used to accelerate meta information generation. Most existing rough set methods assume information system to be centralized and cannot be applied directly in distributed information system. Data integration, which is costly, is necessary for such existing methods. However, meta information integration will eliminate the need of data integration in many cases, since many rough set operations can be done straightforward based on meta information, and many existing methods can be modified based on meta information.
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.
