- 期刊
A Hybrid Fuzzy Clustering Method with a Robust Validity Index
並列摘要
A robust validity index for fuzzy c-means (FCM) algorithm is proposed in this paper. The purpose of fuzzy clustering is to partition a given set of training data into several different clusters that can then be modeled by fuzzy theory. The FCM algorithm has become the most widely used method in fuzzy clustering. Although, there are some successful applications of FCM have been proposed, a disadvantage of FCM is that the number of clusters must be predetermined. After clustering, it is often necessary to evaluate the fitness of the results obtained by FCM. Such assessment techniques are called cluster validity. In this paper, a new cluster validity index is proposed to evaluate the fitness of clusters obtained by FCM and four examples show the results of proposed index have good performances than other cluster validities.
延伸閱讀
- 陳攸伃（2009）。The Development of Hybrid Optimization Algorithm for Fuzzy Clustering〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0006-2606200916555000
- 呂坤奇（2010）。Fuzzy Clustering in Abnormal Behavior Detection〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2010.00697
- 陳昌麟（2019）。Improving the Performance of Hybrid Fuzzy Clustering Algorithm by Using Weighted Method in Parallel System〔碩士論文，嶺東科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0103-1507201914264700
- Nieh, Y. F. (2009). A Novel Validity Criterion for Fuzzy c-regression Model Clustering and its Applications [master's thesis, Tatung University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0081-3001201315103889
- Hsu, J. L., Wu, Y. S., & Wu, I. C. (2014). A Hybrid Method for Sequence Clustering. Journal of Information Science and Engineering, 30(5), 1483-1503. https://doi.org/10.6688/JISE.2014.30.5.11