Chapter 7 - Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations

Abstract

In this chapter, 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 fact, 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, such an approach tends to capture the intrinsic structure of a data set, e.g., the number of clusters.