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Fuzzy clustering is superior to crisp clustering when the boundaries among the clusters are vague and ambiguous. However, the main limitation of both fuzzy and crisp clustering algorithms is their sensitivity to the number of potential clusters and/or their initial positions. Moreover, the comprehensibility of obtained clusters is not expertized, whereupon in data-mining applications, the discovered knowledge is not understandable for human users. To overcome these restrictions, a novel fuzzy rule-based clustering algorithm (FRBC) is proposed in this paper. Like fuzzy rule-based classifiers, the FRBC employs a supervised classification approach to do the unsupervised cluster analysis. It tries to automatically explore the potential clusters in the data patterns and identify them with some interpretable fuzzy rules. Simultaneous classification of data patterns with these fuzzy rules can reveal the actual boundaries of the clusters. To illustrate the capability of FRBC to explore the clusters in data, the experimental results on some benchmark datasets are obtained and compared with other fuzzy clustering algorithms. The clusters specified by fuzzy rules are human understandable with acceptable accuracy.