 Chapter LX Data Clustering Yanchang Zhao University of Technology, Sydney, Australia Longbing Cao University of Technology, Sydney, Australia Huaifeng Zhang University of Technology, Sydney, Australia Chengqi Zhang University of Technology, Sydney, Australia Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. IntroductIon Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, includ- ing well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based cluster- ing, and recent advances in clustering, such as subspace clustering, text clustering and data stream clustering. The major challenges and future trends of data clustering will also be introduced in this chapter. The remainder of this chapter is organized as follows. The background of data clustering will be introduced in Section 2, including the defnition of clustering, categories of clustering techniques, features of good clustering algorithms, and the validation of clustering. Section 3 will present main approaches for clustering, which range from the classic partitioning and hierarchical clustering to recent approaches of bi-clustering and semi- supervised clustering. Challenges and future trends will be discussed in Section 4, followed by the conclusions in the last section. background Data clustering is sourced from pattern recog- nition (Theodoridis & Koutroumbas, 2006), machine learning (Alpaydin, 2004), statistics (Hill & Lewicki, 2007) and database technology (Date, 2003). Data clustering is to partition data into groups, where the data in the same group are similar to one another and the data from different groups are dissimilar (Han & Kamber, 2000). More specifcally, it is to segment data into clusters