Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering

Clustering, an important unsupervised learning task, is very challenging on high-dimensional data, since the generated clusters can be significantly less meaningful as the number of features increases. Feature selection and/or feature weighting can address this issue by selecting and weighting only informative features. These techniques have been extensively studied in supervised learning, e.g. classification, but they are very difficult to use with clustering due to the lack of effective similarity/distance and validation measures. This paper utilises the powerful global search ability of particle swarm optimisation (PSO) on continuous problems, to propose a PSO based method for simultaneous feature selection and feature weighting for clustering on high-dimensional data, where a new validation measure is also proposed as the fitness function of the PSO method. Experiments on datasets with varying dimensionalities and different number of known clusters show that the proposed method can successfully improve clustering performance of different types of clustering algorithms over using the baseline of the original feature set.