Hierarchical and k ‐Means Clustering

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.