聚类分析
单连锁聚类
模糊聚类
确定数据集中的群集数
计算机科学
相关聚类
CURE数据聚类算法
树冠聚类算法
数据挖掘
层次聚类
算法
完整的链接聚类
火焰团簇
星团(航天器)
模糊集
模式识别(心理学)
作者
M.J. Li,Michael K. Ng,Yiu-ming Cheung,Joshua Huang
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2008-11-01
卷期号:20 (11): 1519-1534
被引量:180
摘要
In this paper, we present an agglomerative fuzzy K-means clustering algorithm for numerical data, an extension to the standard fuzzy K-means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters in a data set, which is a well known problem in $k$-means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20000 objects and 100 clusters, and the WINE data set of 178 objects, 17 dimensions and 3 clusters from UCI, have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters.
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