Unsupervised clustering using hyperclique pattern constraints

A novel unsupervised clustering algorithm called hyperclique pattern-KMEANS (HP-KMEANS) is presented. Considering recent success in semi-supervised clustering using pair-wise constraints, an unsupervised clustering method that selects constraints automatically based on Hyperclique patterns is proposed. The COP-KMEANS framework is then adopted to cluster instances of data sets into corresponding groups. Experiments demonstrate promising results compared to classical unsupervised k-means clustering.