Abstract
This paper generalises the concept of grey relational analysis to develop a technique, called grey relational pattern analysis, for analysing the similarity between given patterns. Based on this technique, a clustering algorithm is proposed for finding cluster centres of a given data set. This approach can be categorised as an unsupervised clustering algorithm because it does not need predetermination of appropriate cluster centres in the initialisation. The problem of determining the optimal number of clusters and optimal locations of cluster centres is also considered. Finally, the approach is used to solve several data clustering problems as examples. In each example, the performance of the proposed algorithm is compared with other well-known algorithms such as the fuzzy c-means method and the hard c-means method. Simulation results demonstrate the effectiveness and feasibility of the proposed method.
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