區間資料的聚類演算法

A clustering method for interval data

郭獻駿(Hsien-Chun Kuo)

指導教授 ： 楊敏生

中原大學/理學院/應用數學研究所/碩士(2011年)

https://doi.org/10.6840/cycu201100392

摘要

在模糊聚類方法的領域中，根據資料型態的不同有不同的聚類方法。聚類演算法主要目的是能將給定資料做分群，因此我們提出的聚類演算法是利用Yang and Wu 在2004提出的聚類演算法(SCM)上作延伸，針對區間型資料透過目標函數來求得最佳群組區間代表及最佳群集個數，為了確立此法為好的聚類演算法，我們執行了一些抽樣模擬的資料跟驗證過的實例，結果顯示出區間資料能透過此演算法有好的分類效果。

關鍵字

最佳群集個數 ； SCM ； 區間資料

並列摘要

In fuzzy clustering for different data types, there are many different clustering methods. The main purpose of clustering algorithms is for clustering a given data set. In this thesis, we propose a clustering algorithm by extending Yang and Wu’s clustering algorithm, called SCM, such that it can handle interval data sets with the best representative of the group range and also the best number of clusters. In order to demonstrate this method as a good clustering algorithm, we perform some simulations with sampling data and also some real data sets. The results show that a range of information through this algorithm has good clustering results.

並列關鍵字

the best number of clusters ； interval data ； SCM

參考文獻

[1]L.A. Zadeh, “Fuzzy sets,” Information and Control, Vol. 8, pp. 338-353, 1965.

[2]R.M.C.R. de Souza, F.D.A.T. de Carvalho, “Clustering of interval data based on city–block distances,” Pattern Recognition Letters, Vol. 25, pp.353-365, 2004.

[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, pp.1203-1213, 2004.

[4]F.D.A.T. de Carvalho, P. Brito and H.H. Bock, ”Dynamic Clustering for Interval Data Based on L2 Distance,” Computational Statistics, Vol. 21, pp. 231-250, 2006.

[5]P. D'Urso and P. Giordani, ”A robust fuzzy k-means clustering model for interval valued data,” Computational Statistics, Vol. 21, pp.251-269, 2006.