Chapter 6

Unsupervised Learning via an Iteratively Constructed Clustering Ensemble

Abstract

Boosting and Bagging are the most popular sampling-based ensemble approaches for classification problems. Boosting is considered stronger than Bagging on noise free data set with complex class structures, whereas Bagging is more robust than Boosting in cases where noise data is present. In this chapter, we extend both ensemble approaches to clustering tasks, and present a novel hybrid sampling-based clustering ensemble by combining the strengths of Boosting and Bagging.

Keywords

Bagging; Boosting; Clustering Ensemble; Diversity Measure; Model-based clustering; Sampling; Time series

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