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Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations
ABSTRACT
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 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 for a 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.
Click
tkde2010.pdf
for full text. Click tkde2010_appendix.pdf
for Appendix.