Co-Clustering the Documents and Words Using K-Means Clustering
Volume
4, Issue 5
Pages:
1459-1466
Year of Publication:
October,2016
International Journal of Computer Science and Engineering Communications
ISSN:
2347-8586
| Citation: M.Indirani,P.Revathi."Co-Clustering the Documents and Words Using K-Means Clustering" International Journal of Computer Science and Engineering Communications,Vol.4,Issue.5(2016):1459-1466.
BibTex
@article{indirani2016coclust, author = {M,Indirani.P,Revathi}, title = {Co-Clustering the Documents and Words Using K-Means Clustering}, journal = {International Journal of Computer Science and Engineering Communications}, issue_date = {05}, volume = {4}, number = {5}, month = {Oct}, year = {2016}, issn = {2347-8586}, pages = {1459-1466}, numpages = {8}, url = {http://www.scientistlink.com/ijcsec/2016/V4I514591466.html}, publisher = {Scientist Link Group of Publications}, address = {Chennai, India} } |
| DOI: | Full Text Download |
Abstract:
In this paper, we propose a novel constrained coclustering method to achieve two goals. First, we combine information theoretic coclustering and constrained clustering to improve clustering performance. Second, we adopt both supervised and unsupervised constraints to demonstrate the effectiveness of our algorithm. The unsupervised constraints are automatically derived from existing knowledge sources, thus saving the effort and cost of using manually labeled constraints. To achieve our first goal, we develop a two-sided hidden Markov random field (HMRF) model to represent both document and word constraints. We then use an alternating expectation maximization (EM) algorithm to optimize the model. We also propose two novel methods to automatically construct and incorporate document and word constraints to support unsupervised constrained clustering: 1) automatically construct document constraints based on overlapping named entities (NE) extracted by an NE extractor; 2) automatically construct word constraints based on their semantic distance inferred from 20 Newsgroups Data set. The results of our evaluation using 20 Newsgroups demonstrate the superiority of our approaches against the k-Means similarity approaches.
Keywords:Datamining, Clustering ,K-means clustering and Pattern set mining
In this paper, we propose a novel constrained coclustering method to achieve two goals. First, we combine information theoretic coclustering and constrained clustering to improve clustering performance. Second, we adopt both supervised and unsupervised constraints to demonstrate the effectiveness of our algorithm. The unsupervised constraints are automatically derived from existing knowledge sources, thus saving the effort and cost of using manually labeled constraints. To achieve our first goal, we develop a two-sided hidden Markov random field (HMRF) model to represent both document and word constraints. We then use an alternating expectation maximization (EM) algorithm to optimize the model. We also propose two novel methods to automatically construct and incorporate document and word constraints to support unsupervised constrained clustering: 1) automatically construct document constraints based on overlapping named entities (NE) extracted by an NE extractor; 2) automatically construct word constraints based on their semantic distance inferred from 20 Newsgroups Data set. The results of our evaluation using 20 Newsgroups demonstrate the superiority of our approaches against the k-Means similarity approaches.
Keywords:Datamining, Clustering ,K-means clustering and Pattern set mining
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