An Efficient Algorithm for Complete Linkage Clustering with a Merging Threshold

Abstract

In recent years, one of the serious challenges envisaged by experts in the field of data science is dealing with the gigantic volume of data, piling up at a high speed. Apart from collecting this avalanche of data, another major problem is extracting useful information from it. Clustering is a highly powerful data mining tool capable of finding hidden information from a totally unlabelled dataset. Complete Linkage Clustering is a distance-based Hierarchical clustering algorithm, well-known for providing highly compact clusters. The algorithm because of its high convergence time is unsuitable for large datasets, and hence our paper proposes a preclustering method that not only reduces the convergence time of the algorithm but also makes it suitable for partial clustering of streaming data. The proposed preclustering algorithm uses triangle inequality to take a clustering decision without comparing a pattern with all the members of a cluster, unlike the classical Complete Linkage algorithm. The preclusters are then subjected to an efficient Complete Linkage algorithm for getting the final set of compact clusters in a relatively shorter time in comparison to all those existing variants where the pairwise distance between all the patterns are required for the Clustering process.