Abstract. In this paper we propose the extended star clustering algorithm and compare it with the original star clustering algorithm. We introduce a new concept of star and as a consequence, we obtain different star-shaped clusters. The evaluation experiments on TREC data, show that the proposed algorithm outperforms the original algorithm. Our algorithm is independent of the data order and obtains a smaller number of clusters.

Abstract

In this paper we introduce a general framework for hierarchical clustering that deals with both static and dynamic data sets. From this framework, different hierarchical agglomerative algorithms can be obtained, by specifying an inter-cluster similarity measure, a subgraph of the β-similarity graph, and a cover algorithm. A new clustering algorithm called Hierarchical Compact Algorithm and its dynamic version are presented, which are specific versions of the proposed framework. Our evaluation experiments on several standard document collections show that this algorithm requires less computational time than standard methods in dynamic data sets while achieving a comparable or even better clustering quality. Therefore, we advocate its use for tasks that require dynamic clustering, such as information organization, creation of document taxonomies and hierarchical topic detection.

No abstract

In this paper we present a new parallel clustering algorithm based on the extended star clustering method. This algorithm can be used for example to cluster massive data sets of documents on distributed memory multiprocessors. The algorithm exploits the inherent data-parallelism in the extended star clustering algorithm. We implemented our algorithm on a cluster of personal computers connected through a Myrinet network. The code is portable to different architectures and it uses the MPI message-passing library. The experimental results show that the parallel algorithm clearly improves its sequential version with large data sets. We show that the speedup of our algorithm approaches the optimal as the number of objects increases.