Data clustering is the process of automaticallygrouping data objects into different groups(clusters). The contribution of this book isthreefold: homogeneous clustering of images, pairwiseheterogeneous data co-clustering, and high-orderstar-structured heterogeneous data co-clustering.First, we propose a semantic-based hierarchical imageclustering framework based on multi-user feedback. Bytreating each user as an independent weak classifier,we show thatcombining multi-user feedback is equivalent to thecombinations of weak independent classifiers. Second,we present a novel graph theoretic approach toperform pairwise heterogeneous data co-clustering. Wethen propose Isoperimetric Co-clustering Algorithm, anew method for partitioning the bipartite graph.Lastly, for high-order heterogeneous co-clustering,we propose the Consistent Isoperimetric High-OrderCo-clustering framework to address star-structuredco-cluster
ing problems in which a central data typeis connected to all the other data types. We modelthis kind of data using a k-partite graph andpartition it by considering it as a fusion ofmultiple bipartite graphs.
Prof. Manjeet Rege, Ph.D. is with the Department of ComputerScience at Rochester Institute of Technology. Prof. Ming Dong,Ph.D. is with the Department of Computer Science at Wayne StateUniversity. Their research interests lie in the areas of DataMining, Machine Learning, Information Retrieval, and MultimediaContent Analysis.
