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

This paper proposes a particle swarm optimizer to solve the variable weighting problem in subspace 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 subspace clustering and design a suitable weighting k-means objective function, on which a change of variable weights is exponentially reflected. We transform the original constrained variable weighting problem into a problem with bound constraints using a potential solution coding method and we develop a particle swarm optimizer to minimize the objective function in order to obtain global optima to the variable weighting problem in clustering. Our experimental results on synthetic datasets show that the proposed algorithm greatly improves cluster quality. In addition, the result of the new algorithm is much less dependent on the initial cluster centroids.