1Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Document Clustering with Cluster Refinement and Model Selection Capabilities

Advisor : Dr. Hsu

Presenter : Shu-Ya Li

Authors : Xin Liu, Yihong Gong, Wei Xu,

　　 Shenghuo Zhu

2002 . SIGIR . Page(s) : 191 - 198

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N.Y.U.S.T.

I. M.Outline

Motivation

Objective

Method

Experimental Result

Conclusion

Personal Opinions

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I. M.Motivation

The problems and limitations:

1) The user must formulate the query using the keywords.

2) Traditional text search engines is a narrowly specified search for documents matching the user’s query.

3) Traditional search engine returns hundreds, or even thousands of hits.

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I. M.Objective

We propose a document clustering method that strives to achieve:

1) a high accuracy of document clustering

2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability)

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I. M.Method

Feature Set Term frequencies (TF)

Name entities (NE)

Term pairs (TP)

The documents reporting the Clinton-Lewinsky scandal

The common name entities ： ”Clinton”, ”Lewinsky”, ”Ken Starr”, ”Linda Tripp”, etcThe word pairs ： ”grand jury”, ”independent counsel”, ”supreme court”

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I. M.Method - self-refinement process

Apply the iterative voting scheme to refine the document clusters.

1) GMM + EM algorithm

GMM

EM algorithm

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I. M.Method - self-refinement process

2) Identify discriminative features F = {f1, f2, . . . , fΛ} along with cluster labels S = {σ1, σ2, . . . , σΛ}

Define the discriminative feature metric DFM(fi)

3)

4) Compare the new document cluster set with C.

The result converges →terminate the process Otherwise →set C to the new cluster set, and go to Step 2.

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I. M.Method - Model Selection

measure the similarity between C and C’

The model selection algorithm1) Guess the possible number of document clusters from the data range (Rl,Rh).

2) Set k = Rl.

3) Cluster the document corpus into k clusters.

4) Compute between each pair of the results, and take the average on all the .

5) If k < Rh, k = k + 1, go to Step 3; otherwise, go to Step 6.

6) Select the k which yields the largest average .

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I. M.

1) GMM + EM algorithm

2)

3)

Experimental Result - Document Clustering Evaluation

ABC+CNN-01-13-18-32-48-70-71-77-86[ 新聞機構 - 新聞事件類別 - 報導次數 ]

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I. M.Experimental Result - Model Selection Evaluation

Compared with the BIC-based model selection method

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I. M.Conclusion

To accurately cluster the given document corpus by using the GMM Model together with EM algorithm.

The model selection capability has been achieved by guessing a value C for the number of clusters N.

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I. M.Personal Opinions

Advantage

1) high accuracy of document clustering

2) the model selection capability

Drawback …

Application …