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This paper presents a new fuzzy cover-based clustering algorithm. In the proposed algorithm, the concept of fuzzy cover and objective function are employed to identify holding points in the dataset, and we associate these holding points together to build up the backbones of the final clusters. Three specific objectives underlie the presentation of the proposed approach in this paper. The first is to describe mathematical formulation of the fuzzy covers, and the second is to summarize the detailed procedure of constructing fuzzy covers and splicing them into clusters. The third goal is to demonstrate that this approach is able to find out reasonable representative patterns in the final clusters. We illustrate this approach with four examples in order to verify the clustering effectiveness.