A comparative analysis of granular computing clustering from the view of set

Granular computing (GrC) is a frame computing paradigm that realizes the transformation between two granule spaces with different granularities. A comparative analysis of granular computing clustering is discussed in the paper. Firstly, a granule is defined as the form of vectors by the center and the granularity, especially, an atomic granule is induced by a point which has the granularity 0. Secondly, the join operator realizes the transformation from the granule space with smaller granularity to the granule space with lager granularity, and is used to form the granular computing clustering (GrCC) algorithms. Thirdly, the granular computing clustering algorithms are evaluated from the view of set, such as Global Consistency Error (GCE), Normalized Variation of Information (NVI), and Rand Index (RI). The superiority and feasibility of GrCC are compared with Kmeans and FCM by experiments on the benchmark data sets.