Intelligent control of the hierarchical agglomerative clustering process

The basic process of Hierarchical AgGIomerative (HAG) clustering is describ ed as a merging of clusters based on their proximity. The importance of the selected cluster distance measure in the determination of resulting cluste rs is pointed out. We note a fundamental distinction between the nearest ne ighbor cluster distance measure, Min, and the furthest neighbor measure, Ma x. The first favors the merging of large clusters while the later favors th e merging of smaller clusters. We introduce a number of families of intercl uster distance measures each of which can be parameterized along a scale ch aracterizing their preference for merging larger or smaller clusters, We th en consider the use of this distinction between distance measures as a way of controlling the hierarchical clustering process. Combining this with the ability of fuzzy systems modeling to formalize linguistic specifications, we see the emergence of a tool to add human like intelligence to the cluste ring process.