Document clustering using synthetic cluster prototypes

Article history: Received 17 December 2009 Received in revised form 11 December 2010 Accepted 13 December 2010 Available online 24 December 2010 The use of centroids as prototypes for clustering text documents with the k-means family of methods isnot always thebest choice for representing text clusters due to thehighdimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods. © 2010 Elsevier B.V. All rights reserved.