期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：1
页码：489-491
语种：English
出版社：Ayushmaan Technologies
摘要：There are many application areas ranging from multimedia information processing to temporal data mining in temporal data which are all over in the world. There is a high amount of dependency among temporal data and the proper treatment of data dependency. A temporal data clustering framework via a weighted clustering ensemble of multiple partitions is proposed to produce the initial clustering analysis which is based on unlike temporal data representations. In our approach, to reconcile the initial partitions to candidate the consent partitions from different perspectives, a novel weighted consent function guided by clustering validation criteria is proposed and then introduce an agreement function to further reconcile those candidate consensus partitions to a final partition. As a result, for the joint use of various representations, the proposed weighted clustering group algorithm provides an effectual facilitating technique, which reduce the information loss in a single representation and exploits various information sources underlying temporal data. However, our approach tends to confine the essential structure of a data set. Our approach has been evaluated with standard time series, motion path, and timeseries data stream clustering tasks. For a variety of temporal data clustering tasks the results demonstrate that our approach yields favorite results.
关键词：Temporal Data Clustering;Temporal Data;Data Dependency; Weighted Clustering;Unlike Representations;Data Representations
