Particle swarm optimizer for variable weighting in clustering high-dimensional data

Authors:
Yanping Lu;Shengrui Wang;Shaozi Li;Changle Zhou
Affiliations:
Department of Computer Science, University of Sherbrooke, Sherbrooke, Canada J1K 2R1 and Department of Cognitive Science, Xiamen University, Xiamen, China 361005;Department of Computer Science, University of Sherbrooke, Sherbrooke, Canada J1K 2R1;Department of Cognitive Science, Xiamen University, Xiamen, China 361005;Department of Cognitive Science, Xiamen University, Xiamen, China 361005
Venue:
Machine Learning
Year:
2011

Citing 0
Cited 4

DHCC: Divisive hierarchical clustering of categorical data

Data Mining and Knowledge Discovery
Subspace clustering

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Central clustering of categorical data with automated feature weighting

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Evolving soft subspace clustering

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a particle swarm optimizer (PSO) to solve the variable weighting problem in projected clustering of high-dimensional data. Many subspace clustering algorithms fail to yield good cluster quality because they do not employ an efficient search strategy. In this paper, we are interested in soft projected clustering. We design a suitable k-means objective weighting function, in which a change of variable weights is exponentially reflected. We also transform the original constrained variable weighting problem into a problem with bound constraints, using a normalized representation of variable weights, and we utilize a particle swarm optimizer to minimize the objective function in order to search for global optima to the variable weighting problem in clustering. Our experimental results on both synthetic and real data show that the proposed algorithm greatly improves cluster quality. In addition, the results of the new algorithm are much less dependent on the initial cluster centroids. In an application to text clustering, we show that the algorithm can be easily adapted to other similarity measures, such as the extended Jaccard coefficient for text data, and can be very effective.