Single Link clustering on data sets
Ajaya Kushwaha, Manojeet Roy · 2012
Abstract — Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups w ith low distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Most data-mining methods assume data is in the form of a feature-vector (asingle relational table) and cannot handle multi-relational data. Inductive logic programming is a form of relational data mining that discovers rules in _first-order logic from multi-relational data. This paper discusses the application of SLINK to learning patterns for link discovery. Clustering is among the oldest techniques used in data mining applications. Typical implementations of the hierarchical agglomerative clustering methods (HACM) require an amount of O ( N 2)-space when there are N data objects, making such algorithms impractical for problems involving large datasets. The well-known clustering algorithm RNN-CLINK requires only O(N)-space but O(N 3)-time in the w orst case, although the average time appears to be O(N 2 log N). Index Terms—clustering, nearest neighbor, reciprocal nearest neighbor, complete link, probabilistic analysis.