Effective data summarization for hierarchical clustering in large datasets

Abstract

Cluster analysis in a large dataset is an interesting challenge in many fields of Science and Engineering. One important clustering approach is hierarchical clustering, which outputs hierarchical (nested) structures of a given dataset. The single-link is a distance-based hierarchical clustering method, which can find non-convex (arbitrary)-shaped clusters in a dataset. However, this method cannot be used for clustering large dataset as this method either keeps entire dataset in main memory or scans dataset multiple times from secondary memory of the machine. Both of them are potentially severe problems for cluster analysis in large datasets. One remedy for both problems is to create a summary of a given dataset efficiently, and the summary is subsequently used to speed up clustering methods in large datasets. In this paper, we propose a summarization scheme termed data sphere (ds) to speed up single-link clustering method in large datasets. The ds utilizes sequential leaders clustering method to collect important statistics of a given dataset. The single-link method is modified to work with ds. Modified clustering method is termed as summarized single-link (SSL). The SSL method is considerably faster than the single-link method applied directly to the dataset, and clustering results produced by SSL method are close to the clustering results produced by single-link method. The SSL method outperforms single-link using data bubble (summarization scheme) both in terms of clustering accuracy and computation time. To speed up proposed summarization scheme, a technique is introduced to reduce a large number of distance computations in leaders method. Experimental studies demonstrate effectiveness of the proposed summarization scheme for large datasets.