Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
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
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Modern Information Retrieval
Document clustering with cluster refinement and model selection capabilities
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Algorithms
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Comparison of the Stability Characteristics of Some Graph Theoretic Clustering Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
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Document clustering techniques mostly depend on models that impose explicit and/or implicit priori assumptions as to the number, size, disjunction characteristics of clusters, and/or the probability distribution of clustered data. As a result, the clustering effects tend to be unnatural and stray away more or less from the intrinsic grouping nature among the documents in a corpus. We propose a novel graph-theoretic technique called Clique Percolation Clustering (CPC). It models clustering as a process of enumerating adjacent maximal cliques in a random graph that unveils inherent structure of the underlying data, in which we unleash the commonly practiced constraints in order to discover natural overlapping clusters. Experiments show that CPC can outperform some typical algorithms on benchmark data sets, and shed light on natural document clustering.