Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
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
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
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
Performance evaluation of constraints in graph-based semi-supervised clustering
AMT'10 Proceedings of the 6th international conference on Active media technology
Toward finding hidden communities based on user profile
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
We propose a graph model for clustering based on mutual information and show that the clustering problem can be approximated as a combinatorial problem over the proposed graph model. Based on the stationary distribution induced from the problem setting, we propose a function which measures the relevance among data objects. This function enables to represent the entire objects as an edge-weighted graph, where pairs of objects are connected by the 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. We demonstrate the effectiveness of the proposed approach over the document clustering problem. The results are encouraging and indicate the effectiveness of our approach.