A Scalable Hierarchical Clustering Algorithm Using Spark

Abstract

Clustering is often an essential first step in data mining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can offer a richer representation by suggesting the potential group structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of single-linkage clustering algorithm due to its natural expression of iterative process. Our algorithm can be deployed easily in Amazon's cloud environment. And a thorough performance evaluation in Amazon's EC2 verifies that the scalability of our algorithm sustains when the datasets scale up.