C4.5: programs for machine learning
C4.5: programs for machine learning
Journal of the ACM (JACM)
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
The b-chromatic number of a graph
Discrete Applied Mathematics
ACM Computing Surveys (CSUR)
A clustering algorithm based on graph connectivity
Information Processing Letters
Concept decompositions for large sparse text data using clustering
Machine Learning
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering validity checking methods: part II
ACM SIGMOD Record
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
A tutorial on spectral clustering
Statistics and Computing
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Neighborhood graphs for indexing and retrieving multi-dimensional data
Journal of Intelligent Information Systems
A new greedy algorithm for improving b-coloring clustering
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Toward improving re-coloring based clustering with graph b-coloring
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Combinatorial markov random fields
ECML'06 Proceedings of the 17th European conference on Machine Learning
Spatio-temporal feature-based keyframe detection from video shots using spectral clustering
Pattern Recognition Letters
Toward finding hidden communities based on user profile
Journal of Intelligent Information Systems
A re-coloring approach for graph b-coloring based clustering
International Journal of Knowledge-based and Intelligent Engineering Systems
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We propose a graph model for mutual information based clustering problem. This problem was originally formulated as a constrained optimization problem with respect to the conditional probability distribution of clusters. Based on the stationary distribution induced from the problem setting, we propose a function which measures the relevance among data objects under the problem setting. This function is utilized to capture the relation among data objects, and the entire objects are represented as an edge-weighted graph where pairs of objects are connected with edges with their relevance. We show that, in hard assignment, the clustering problem can be approximated as a combinatorial problem over the proposed graph model when data is uniformly distributed. By representing the data objects as a graph based on our graph model, various graph based algorithms can be utilized to solve the clustering problem over the graph. The proposed approach is evaluated on the text clustering problem over 20 Newsgroup and TREC datasets. The results are encouraging and indicate the effectiveness of our approach.