Algorithms for clustering data
Algorithms for clustering data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Concept decompositions for large sparse text data using clustering
Machine Learning
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering Algorithms
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Document clustering by concept factorization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A general model for clustering binary data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
A new multiobjective clustering technique based on the concepts of stability and symmetry
Knowledge and Information Systems
Nonnegative Matrix Factorization on Orthogonal Subspace
Pattern Recognition Letters
Knowledge and Information Systems
GIS enabled service site selection: Environmental analysis and beyond
Information Systems Frontiers
A methodological approach to mining and simulating data in complex information systems
Intelligent Data Analysis
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Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. Recently, a number of methods have been proposed and demonstrated good performance based on matrix approximation. Despite significant research on these methods, few attempts have been made to establish the connections between them while highlighting their differences. In this paper, we present a unified view of these methods within a general clustering framework where the problem of clustering is formulated as matrix approximations and the clustering objective is minimizing the approximation error between the original data matrix and the reconstructed matrix based on the cluster structures. The general framework provides an elegant base to compare and understand various clustering methods. We provide characterizations of different clustering methods within the general framework including traditional one-side clustering, subspace clustering and two-side clustering. We also establish the connections between our general clustering framework with existing frameworks.