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Hierarchical and k-Means Clustering
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Book Chapter
Hierarchical and k-Means Clustering
2014
Overview
Clustering algorithms seek to segment the entire data set into relatively homogeneous subgroups or clusters. Clustering is often performed as a preliminary step in a data mining process. This chapter discusses about the hierarchical clustering methods and describes k‐means clustering algorithm. In hierarchical clustering, a treelike cluster structure is created through recursive partitioning (divisive methods) or combining (agglomerative) of existing clusters. Single‐linkage clustering seeks the minimum distance between any records in two clusters. Complete‐linkage clustering seeks to minimize the distance among the records in two clusters that are farthest from each other. The k‐means clustering algorithm is a straightforward and effective algorithm for finding clusters in data. The Enterprise Miner clustering node uses SAS's FASTCLUS procedure, a version of the k‐means algorithm.
Publisher
John Wiley & Sons, Incorporated,John Wiley & Sons, Inc
Subject
ISBN
9780470908747, 0470908742
