Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21245
Title: A robust adaptive clustering analysis method for automatic identification of clusters
Authors: Mok, PY 
Huang, HQ
Kwok, YL
Au, JS 
Keywords: Cluster analysis
Cluster ensembles
Cluster validity
Fuzzy C-Means
Fuzzy clustering
Issue Date: 2012
Publisher: Elsevier
Source: Pattern recognition, 2012, v. 45, no. 8, p. 3017-3033 How to cite?
Journal: Pattern recognition 
Abstract: Identifying the optimal cluster number and generating reliable clustering results are necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis relies not only on the assumption of cluster number but also on the clustering algorithm employed. This paper proposes a new clustering analysis method that identifies the desired cluster number and produces, at the same time, reliable clustering solutions. It first obtains many clustering results from a specific algorithm, such as Fuzzy C-Means (FCM), and then integrates these different results as a judgement matrix. An iterative graph-partitioning process is implemented to identify the desired cluster number and the final result. The proposed method is a robust approach as it is demonstrated its effectiveness in clustering 2D data sets and multi-dimensional real-world data sets of different shapes. The method is compared with cluster validity analysis and other methods such as spectral clustering and cluster ensemble methods. The method is also shown efficient in mesh segmentation applications. The proposed method is also adaptive because it not only works with the FCM algorithm but also other clustering methods like the k-means algorithm.
URI: http://hdl.handle.net/10397/21245
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2012.02.003
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