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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.19 No.2
- 발행연도
- 2019.6
- 수록면
- 97 - 102 (6page)
- DOI
- 10.5391/IJFIS.2019.19.2.97
이용수
초록· 키워드
오류제보하기
This paper proposes a method for improving performance of clustering algorithm. Among unsupervised learning methods such as clustering, K-means has the advantage of being widely used and simple to implement, but has problems that is heavily influenced by initial centroids and may be got stuck at local minimum. To minimize these shortcomings, this work uses a clustering method with a random swap algorithm based on K-means++ that calculates the distance between data and the center point as a probability. The swap-based clustering method replaces the existing centroids and the concentrated centroids with each other during clustering steps, and then performing re-partitions, and fine-tuning steps with K-means++. The experimental results show the swap-based K-means++ clustering method proposed in this paper has better performance than other methods, as comparing the method proposed with the method of other clustering under various circumstances. As producing datasets of vehicles on general purpose for measuring performance in various experimental environments, we demonstrate the excellence of the proposed method.
목차
Abstract
1. Introduction
2. K-means++ Clustering
3. The Proposed Random Swap based Clustering Algorithm
4. Experimental Results
5. Conclusions
References
참고문헌 (14)
참고문헌 신청
J. Chen / 2011 / Swap-based clustering for location-based services
M. C. Su / 2001 / A modified version of the K-means algorithm with a distance based on cluster symmetry / IEEE Transactions on Pattern Analysis & Machine Intelligence 23(6):674~680
Jiahu Qin / 2017 / Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory / IEEE Transactions on Cybernetics 47(3):772~783
김민영 / 2017 / Simultaneous Learning of Sentence Clustering and Class Prediction for Improved Document Classification / International Journal of Fuzzy Logic and Intelligent systems 17(1):35~42
D. Arthur / 2007 / -means++: the advantages of careful seeding / Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms :1027~1035
M. C. Su / 2001 / A modified version of the K-means algorithm with a distance based on cluster symmetry / IEEE Transactions on Pattern Analysis & Machine Intelligence 23(6):674~680
Jiahu Qin / 2017 / Distributed $k$ -Means Algorithm and Fuzzy $c$ -Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory / IEEE Transactions on Cybernetics 47(3):772~783
김민영 / 2017 / Simultaneous Learning of Sentence Clustering and Class Prediction for Improved Document Classification / International Journal of Fuzzy Logic and Intelligent systems 17(1):35~42
D. Arthur / 2007 / -means++: the advantages of careful seeding / Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms :1027~1035
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