A survey of Clustering Algorithms.

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This chapter presents a tutorial overview of the main clustering methods used in Data Mining. The goal is to provide a self-contained review of the concepts and the mathematics underlying clustering techniques. The chapter begins by providing measures and criteria that are used for determining whether two objects are similar or dissimilar. Then the clustering methods are presented, divided into: hierarchical, partitioning, density-based, model-based, grid-based, and soft-computing methods. Following the methods, the challenges of performing clustering in large data sets are discussed. Finally, the chapter presents how to determine the number of clusters.
Original languageEnglish
Title of host publicationData Mining and Knowledge Discovery Handbook
PublisherSpringer, Boston, MA
Number of pages30
ISBN (Electronic)978-0-387-09823-4
ISBN (Print)978-0-387-09822-7
StatePublished - 7 Jul 2010


  • Clustering
  • K-means
  • Intra-cluster homogeneity
  • Inter-cluster separability


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