區間資料的模糊聚類演算法

Fuzzy clustering algorithms for interval data

黃文志(HUANG-WEN ZHI)

指導教授 ： 楊敏生

中原大學/理學院/應用數學研究所/碩士(2014年)

https://doi.org/10.6840/cycu201400066

摘要

在模糊聚類分析中，模糊 C 均值 (fuzzy c-means, FCM) 聚類演算法被廣泛的使用。聚類分析最主要目地是將給定資料做分群，我們提出的聚類演算法是利用Yang and Wu [6]在2002提出的替代式模糊C均值聚類演算法(Alternative Fuzzy C-Means、簡稱為AFCM)上作延伸，使其能應用在區間資料上，我們稱為Interval AFCM，簡稱為IAFCM，透過目標函數來求得最佳群組區間代表之演算法則，為了確定此區間資料聚類法為好的聚類演算法則，我們將執行一些模擬資料跟驗證過的實例，並與 de Carvalho [2]區間模糊C 均值 (Interval Fuzzy C-Means， IFCM) 做比較，經由實驗比較後我們發現IAFCM 有良好的聚類結果。

關鍵字

區間模糊C 均值，區間替代式模糊C 均值，區間資料

並列摘要

In cluster analysis, the fuzzy c-means (FCM) clustering algorithm is the most used method. The main purpose of clustering analysis is for clustering a given data set. In this thesis,we propose a clustering algorithm by extending Yang and Wu [6] clustering algorithm, called AFCM, such that it can handle interval data with the best representative. In order to show this method as a good clustering algorithm, we perform some simulations with sampling data and also some real data sets,and also compare it with interval Fuzzy C-Means (IFCM) proposed by de Carvalho [2]. The results show that the proposed method has good clustering results.

並列關鍵字

interval data ； IFCM ； IAFCM

參考文獻

[1] J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function

Algorithms,Plenum Press, New York, 1981.

[2] F.D. A.T. de Carvalho, Fuzzy c-means clustering methods for symbolic

[3] D.S. Guru, B.B. Kiranagi, P. Nagabhushan, Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns, Pattern Recognition Letters, vol. 25, 2004 pp.1203-1213,

[5] W.L.Hung, M.S.Yang , Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation ,Fuzzy Sets and Systems vol.150 2005 pp.561–577