Delta-MSE dissimilarity in suboptimal K-Means clustering

K-Means clustering is a well-known partition-based technique in unsupervised learning to construct pattern models. The main difficulty, however, is that its performance is highly susceptible to the initialized partition. To attack this problem, a suboptimal K-Means algorithm is briefly reviewed by applying dynamic programming over the principal component direction. In particular, a heuristic clustering dissimilarity, the Delta-MSE function, is incorporated into the suboptimal K-Means algorithm. The Delta-MSE function is derived by calculating the difference of within-class variance before and after moving a given data sample from one cluster to another. Experimental results show that the suboptimal K-Means algorithm that uses the Delta-MSE dissimilarity generally outperforms the original L₂ distance based suboptimal algorithm and a specific kd-tree clustering algorithm.