I. Dhillon, S. Mallela, and R. Kumar, "A Divisive InformationTheoretic Feature Clustering Algorithm for Text Classification," J. Machine Learning Research, vol. 3, 2003.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and Retrieval Clustering General Terms Algorithms Keywords Word Clustering, Feature Dimensionality Reduction 1. INTRODUCTION Word clustering techniques have been successfully used for text classification, with two main advantages: dimension reduction and improving classification accuracy [1, 2, 3, 6]. Information theoretic approach to word clustering considers the word distributions over categories to determine similar words. Such methods need labeled training data. Instead, we introduce a rule based, context dependent word clustering method, with the rules extracted from various domain ....

I. Dhillon, S. Manella, and R. Kumar. A divisive information -theoretic feature clustering for text classification. to appear in Machine Learning Research (JMLR), 2002.

No context found.

I. S. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classification. J. of Mach. Learning Res., 3:1265--1287, 2003.

No context found.

I. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classi cation. Journal of Machine Learning Research, 3(4):1265-1287, 2003.

No context found.

I. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classi cation. Journal of Machine Learning Research, 3(4):1265-1287, 2003.

.... X;Y ) where re ects the tradeo between compression and preservation of mutual information. The resulting algorithm yields a soft clustering of the data using a deterministic annealing procedure. For a hard partitional clustering algorithm using a similar information theoretic framework, see [6]. These algorithms were proposed for one sided clustering. An agglomerative hard clustering version of the IB method was used in [19] to cluster documents after clustering words. The work in [8] extended the above work to repetitively cluster documents and then words. Both these papers use ....

....and approximations till the optimal is discovered for the example p(X; Y ) given in Section 2. set consists of approximately 20; 000 newsgroup articles collected evenly from 20 di erent usenet newsgroups. This data set has been used for testing several supervised text classi cation tasks [6] and unsupervised document clustering tasks [19, 8] Many of the newsgroups share similar topics and about 4:5 of the documents are cross posted making the boundaries between some news groups rather fuzzy. To make our comparison consistent with previous algorithms we reconstructed various subsets ....

[Article contains additional citation context not shown here]

I. S. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classi cation. Journal of Machine Learning Research(JMLR): Special Issue on Variable and Feature Selection, 3:1265-1287, March 2003.

....using a procedure similar to the deterministic annealing approach of [16] A greedy agglomerative hard clustering version of the Information Bottleneck algorithm was used in [1, 19] to cluster words in order to reduce feature size for supervised text classi cation. For this same task, recently [6] proposed a divisive hard clustering algorithm that directly minimizes the loss in mutual information and was found to result in higher classi cation accuracies than [1, 19] All these algorithms were proposed for one sided clustering. An agglomerative hard clustering version of the Information ....

....[13] and the SMART collection from Cornell (ftp: ftp.cs.cornell.edu pub smart) The NG20 data set consists of approximately 20; 000 newsgroup articles collected evenly from 20 di erent usenet news groups. This data set has been used for testing several supervised text classi cation tasks [1, 19, 14, 6] and un supervised document clustering tasks [18, 8] Many of the news groups share similar topics and about 4:5 of the documents are cross posted making the boundaries between some news groups rather fuzzy. To make our comparison consistent with previous algorithms we reconstructed various ....

I. S. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classi cation. Journal of Machine Learning Research(JMLR): Special Issue on Variable and Feature Selection, 3:1265-1287, March 2003.

No context found.

I. Dhillon, S. Mallela, and R. Kumar, "A Divisive InformationTheoretic Feature Clustering Algorithm for Text Classification," J. Machine Learning Research, vol. 3, 2003.

No context found.

Dhillon, I., Mallela, S., Kumar, R.: A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification. Journal of Machine Learning Research 3 (2003) 1265-1287

No context found.

Dhillon, I.S., Mallela, S., Kumar, R.: A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research 3 (2003) 1265--1287

No context found.

I. Dhillon, S. Mallelaa, R. Kumar. A Divisive InformationTheoretic Feature Clustering Algorithm for Text Classification. To appear in the Journal of Machine Learning Research,: Special Issue on Variable and Feature Selection, Vol. 3 pp. 1265-1287, 2003.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC