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

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.