한글 웹 문서 클러스터링 성능향상을 위한 자질선정 기법 비교 연구 = A Comparative Study of Feature Selection Methods for Korean Web Documents Clustering

다국어 초록 (Multilingual Abstract)

This Paper is a comparative study of feature selection methods for Korean web documents clustering. First, we focused on how the term feature and the co-link of web documents affect clustering performance. We clustered web documents by native term feature, co-link and both, and compared the output results with the originally allocated category. And we selected term features for each category using $X^2$, Information Gain (IG), and Mutual Information (MI) from training documents, and applied these features to other experimental documents. In addition we suggested a new method named Max Feature Selection, which selects terms that have the maximum count for a category in each experimental document, and applied $X^2$ (or MI or IG) values to each term instead of term frequency of documents, and clustered them. In the results, $X^2$ shows a better performance than IG or MI, but the difference appears to be slight. But when we applied the Max Feature Selection Method, the clustering Performance improved notably. Max Feature Selection is a simple but effective means of feature space reduction and shows powerful performance for Korean web document clustering.

참고문헌 (Reference)

1 "웹 문서중 의미 있는 표의 추출." 332-339, 2002.

2 "문서관리를 위한 자동문서범주화에 대한 이론 및 기법." 2002.

3 "동시링크를 이용한 웹 문서 클러스터링 실험." 233-253, 2003.

4 "“Webpage clustering using a self-organizing map of user navigation patterns" 35 : 245-256, 2003.

5 "“WTMS: a system for collecting and analyzing topicspecific Web information"" 457-471, 2000.

6 "“Trawlingthe Web for emerging cybercommunities Proceedings of the8th WWW Conference." 1999.

7 "“Training algorithms for lineartext classifier Proc. of the 19thAnnual International ACM-SIGIR" 298-315, 1996.

8 "“Syntacticclustering of the Web" 391-404, 1997.

9 "“Organizing topicspecificWeb information" 133-141, 2000

10 "“Information Retrieval on the World Wide Web and Active Logic" 2003.

11 "“Distributional clusteringof words for text classification Proc. of the 21th Annual InternationalACM-SIGIR." 1998.

12 "“Criterionfunctions for document clustering- experiment and analysis Department of Computer Science University of Minnesota" 2001.

13 "“Clustering ofweb documents with the use ofterm frequency and co-link inhypertext The 3rd Asia PacificInternational symposium on InformationTechnology" 122-127, 2004

14 "“A comparison of two learningalgorithms for text categorization Proc. of the 3rd AnnualSymposium on Document Analysisand Information Retrieval" 96-103, 1998.

15 "Second InternationalConference on Advancesin Web - Age Information management"

16 "R. R. “Bibliometrics of theWorld Wide Web An ExploratoryAnalysis of the Intellectual Structureof Cyberspace Proceedings of the1996 American Society for InformationScience Annual Meeting." 1996.

17 "Proceedings of the Conference onHuman Factors in Computing Systems" 213-220,

18 "Proceedings of the 7th InternationalWWW Conference." 1998

19 "Proc. of International Conferenceon SIGMOD '98" 307-318, 1998.

20 "Department of ComputerScience University of Minnesota." 2002.

21 "ACognitive perspective on search enginetechnology and the WWW.Cambridge University Press." 2000.

22 "A Study ofUser Queries On The Web"

23 "1997.“A comparative study on featureselection in text categorization Proceeding of ICML-97 14th InternationalConference on MachineLearning."

24 "1973. “Co-citation in the scientificliterature A new measure ofthe relationship between two documents Journal of American societyfor Information Science. vol.24" 265-269,

25 ".“web document clustering usinghyperlink structures 19-45." 41 : 2002