Different Representations Ensemble with Temporal Data Clustering Via Weighted Clustering
1Vamsi Krishna Jayanti and 2Vasantha Kumar
1Department of Information Technology, Email: firstname.lastname@example.org,
2Department of Computer Science and Engineering, Avanthi Institute oF Technology, Makavanipalem, Narsipatnam, Andhra Pradesh, India.
Temporal data clustering provides underpinning techniques for discovering the intrinsic structure and condensing information over temporal data. In this paper, we present a temporal data clustering framework via a weighted clustering ensemble of multiple partitions produced by initial clustering analysis on different temporal data representations. In our approach, we propose a novel weighted consensus function guided by clustering validation criteria to reconcile initial partitions to candidate consensus partitions from different perspectives, and then, introduce an agreement function to further reconcile those candidate consensus partitions to a final partition. As a result, the proposed weighted clustering ensemble algorithm provides an effective enabling technique for the joint use of different representations, which cuts the information loss in a single representation and exploits various information sources underlying temporal data. In addition, our approach tends to capture the intrinsic structure of a data set, e.g., the number of clusters. Our approach has been evaluated with benchmark time series, motion trajectory, and time-series data stream clustering tasks. Simulation results demonstrate that our approach yields favorite results fora variety of temporal data clustering tasks. As our weighted cluster ensemble algorithm can combine any input partitions to generate a clustering ensemble, we also investigate its limitation by formal analysis and empirical studies.
International eJournal of Mathematical Sciences, Technology and Humanities
Volume 2, Issue 5, Pages: 830 - 837