电力管网分布区域广、数据量大,给电力管网地理信息系统(geographic information system,GIS)的实时响应性能和操作平滑性能提出了挑战。作者对电力管网GIS空间数据处理问题进行了研究。在图层删选中,提出了一种基于模糊聚类的图层关联...电力管网分布区域广、数据量大,给电力管网地理信息系统(geographic information system,GIS)的实时响应性能和操作平滑性能提出了挑战。作者对电力管网GIS空间数据处理问题进行了研究。在图层删选中,提出了一种基于模糊聚类的图层关联分析方法;在空间数据组织中,采用基于"四倍原则"的无缝空间数据分块分级组织方法。最后对系统数据库的选择提出了建议。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
文摘电力管网分布区域广、数据量大,给电力管网地理信息系统(geographic information system,GIS)的实时响应性能和操作平滑性能提出了挑战。作者对电力管网GIS空间数据处理问题进行了研究。在图层删选中,提出了一种基于模糊聚类的图层关联分析方法;在空间数据组织中,采用基于"四倍原则"的无缝空间数据分块分级组织方法。最后对系统数据库的选择提出了建议。
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.
