A performance comparison of using PCA-based feature reduction and ant colony optimization with soft clustering approaches

This paper proposes a performance comparison using principal component analysis (PCA)-based feature reduction method, and ant colony optimization algorithm combining with soft clustering approaches. Two particular fuzzy clustering, fuzzy c-means (FCM) and k-harmonic means (KHM) are used for empirical tests. PCA, a linear feature reduction applied here is employed as preprocess of soft clustering approaches for relieving the curse of high-dimensional, noisy data. Ant colony optimization algorithm is employed as the first level of clustering that supplies the optimal set of initial clusters to those soft clustering methods. Comparison tests among related methods, PCA-FCM, PCA-KHM, ANT-FCM and ANT-KHM are evaluated in terms of clustering objective function, adjusted rand index and time consumption. Seven well-known benchmark realworld data sets are employed in the experiments. Within the scope of this study, the superiority of using PCA for feature reduction over the two-level clustering, ANT-FCM and ANTKHM is pointed out.