Unsupervised clustering using hyperclique pattern constraints

A novel unsupervised clustering algorithm called Hyperclique Pattern-KMEANS (HP-KMEANS) is presented. Considering recent success in semisupervised 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.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Yuchou Chang, Dah-Jye Lee, James K. Archibald, Yi Hong
• Keywords: Computer Vision, ICPR 2008, Unsupervised Clustering, Unsupervised Clustering Algorithm, Unsupervised Clustering Method