Advancing data clustering via projective clustering ensembles

Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering ensembles and projective clustering. Specifically, PCE enables clustering ensemble methods to handle ensembles composed by projective clustering solutions. PCE has been formalized as an optimization problem with either a two-objective or a single-objective function. Two-objective PCE has shown to generally produce more accurate clustering results than its single-objective counterpart, although it can handle the object-based and feature-based cluster representations only independently of one other. Moreover, both the early formulations of PCE do not follow any of the standard approaches of clustering ensembles, namely instance-based, cluster-based, and hybrid. In this paper, we propose an alternative formulation to the PCE problem which overcomes the above issues. We investigate the drawbacks of the early formulations of PCE and define a new ...

• Type: Journal
• Year: 2011
• Where: SIGMOD
• Authors: Francesco Gullo, Carlotta Domeniconi, Andrea Tagarelli
• Keywords: Database, Ensemble Methods, Information Storage And Retrieval, Objective Function, SIGMOD 2011