Seed-Detective: A Novel Clustering Technique Using High Quality Seed for K-Means on Categorical and Numerical Attributes

In this paper we present a novel clustering technique called Seed-Detective. It is a combination of modified versions of two existing techniques namely Ex-Detective and Simple K-Means. Seed-Detective first discovers a set of preliminary clusters using our modified Ex-Detective. The modified Ex-Detective allows a data miner to assign different weights (importance levels) for all attributes, both numerical and categorical. Centers of the preliminary clusters are then considered as initial seeds for the modified Simple K-Means, which unlike existing Simple K-Means does not randomly select the initial seeds. Centers of the preliminary clusters are naturally expected to be better quality seeds than the seeds that are chosen randomly. Having better quality initial seeds as input the modified Simple K-Means is expected to produce better quality clusters. We compare Seed-Detective with several existing techniques including Ex-Detective, Simple KMeans, Basic Farthest Point Heuristic (BFPH) and New Farthest Point Heuristic (NFPH) on two publicly available natural data sets. BFPH and NFPH were shown in the literature to be better than Simple K-Means. However, our initial experimental results indicate that Seed-Detective produces better clusters than other techniques, based on several evaluation criteria including F-measure, entropy and purity. Another contribution of this paper is the experimental result on Ex-Detective which was never tested before. .