A robust adaptive clustering analysis method for automatic identification of clusters

• Published in: Pattern recognition 2012-08, Vol.45 (8), p.3017-3033
• Main Authors: Mok, P.Y., Huang, H.Q., Kwok, Y.L., Au, J.S.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Cluster analysis; Cluster ensembles; Cluster validity; Clustering; Clusters; Exact sciences and technology; Fuzzy; Fuzzy C-Means; Fuzzy clustering; Fuzzy logic; Information, signal and communications theory; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Spectra; Tasks; Telecommunications and information theory
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2012.02.003

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. ► A new approach for clustering analysis. ► Adaptive to different clustering algorithms. ► Identification of desired cluster numbers. ► Effective in handling data set with various data shapes. ► Comparative to spectral clustering and cluster ensembles.