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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.4
- 발행연도
- 2020.12
- 수록면
- 346 - 357 (12page)
- DOI
- 10.5391/IJFIS.2020.20.4.346
이용수
초록· 키워드
오류제보하기
Fuzzy C-means (FCM) has a definite limitation when partitioning a dataset into clusters with varying sizes and densities because it ignores the scale difference in different dimensions of input data objects. To alleviate this cluster size insensitivity, we propose a wrapper algorithm for FCM by introducing cluster size as a priori information and limiting the search direction on the basis of density benchmarks (CSCD-FCM). This method is divided into two stages. The first stage adjusts the position of each cluster while maintaining its shape, and the second stage changes the shape of each cluster while maintaining its center. Both steps modify fuzzy partitions generated by FCM-like soft clustering methods by optimizing a “size-constrained” objective function. Numerical and practical experiments with unbalanced cluster size settings demonstrate the effectiveness of this method for extracting actual cluster structures, as well as achieving the desired cluster populations.
목차
Abstract
1. Introduction
2. An Overview of Fuzzy C-Means
3. Cluster Size-Constrained Fuzzy C-Means with Density Center Searching (CSCD-FCM)
4. Experiment
5. Discussion
6. Conclusion
References
참고문헌 (29)
참고문헌 신청
L. Rokach / 2005 / Data Mining and Knowledge Discovery Handbook / Springer :321~352
O. Maimon / 2005 / Data Mining and Knowledge Discovery Handbook / Springer
C. Fraley / 1998 / How many clusters? Which clustering method? Answers via model-based cluster analysis / The Computer Journal 41(8):578~588
J. Nayak / 2015 / Computational Intelligence in Data Mining / Springer :133~149
P. K. Nizar Banu / 2015 / Performance Analysis of Hard and Soft Clustering Approaches For Gene Expression Data / International Journal of Rough Sets and Data Analysis 2(1):58~69
O. Maimon / 2005 / Data Mining and Knowledge Discovery Handbook / Springer
C. Fraley / 1998 / How many clusters? Which clustering method? Answers via model-based cluster analysis / The Computer Journal 41(8):578~588
J. Nayak / 2015 / Computational Intelligence in Data Mining / Springer :133~149
P. K. Nizar Banu / 2015 / Performance Analysis of Hard and Soft Clustering Approaches For Gene Expression Data / International Journal of Rough Sets and Data Analysis 2(1):58~69
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