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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2007.8
- 수록면
- 115 - 118 (4page)
이용수
초록· 키워드
오류제보하기
Clustering is the process of dividing a given set of data to find hidden meaning in the data set. It is useful for the analysis of data that are difficult for people to examine one by one, by focusing on clusters of data having similar propensities. Clustering, which is used in various fields, is useful for document analysis, but if the number of documents and of words is too large, it would be difficult for people to interpret it. This paper proposed an Attraction Map that aims to solve this problem by visualizing documents on a two-dimensional plane. First of all, it visualized the centers of clusters in the way of dimensional reduction and then, visualized documents by using the relationship between the centers of clusters and documents. The performance of the Attraction Map was fast enough to allow for real-time operation. In addition, the Attraction Map arranged documents having similar propensities near to each other in consideration of their relationship with clusters, so it was useful for interpreting the results of clusters.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. PROPOSED METHOD
4. EXPERIMENTAL RESULTS
5. CONCLUSIONS
6.ACKNOWLEDGEMENTS
7. REFERENCES
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UCI(KEPA) : I410-ECN-0101-2012-004-004265246