Data Clustering

Chapter 22

Cluster Ensembles: Theory and Applications

Joydeep Ghosh

University of Texas at Austin Austin, [e-mail]

Ayan Acharya

University of Texas at Austin Austin, [e-mail]

22.1 Introduction

The design of multiple classifier systems to solve difficult classification problems, using techniques such as bagging, boosting, and output combining [54, 62, 38, 36], has resulted in some of the most notable advances in classifier design over the past two decades. A popular approach is to train multiple “base” classifiers, whose outputs are combined to form a classifier ensemble. A survey of such ensemble techniques—including applications of them to many difficult real-world problems such as remote sensing, person recognition, ...