It is to find clusters in subspaces, where two clusters may exist in two different subspaces and the subspaces may also have different dimensionalities.
Published in Chapter:
Data Clustering
Yanchang Zhao (University of Technology - Sydney, Australia), Longbing Cao (University of Technology - Sydney, Australia), Huaifeng Zhang (University of Technology - Sydney, Australia), and Chengqi Zhang (University of Technology - Sydney, Australia)
Copyright: © 2009
|Pages: 11
DOI: 10.4018/978-1-60566-242-8.ch060
Abstract
Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering techniques, such as partitioning clustering, hierarchical clustering, density-based clustering and grid-based clustering, 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 definition 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 semisupervised clustering. Challenges and future trends will be discussed in Section 4, followed by the conclusions in the last section.