Gu, Xiaowei and Angelov, Plamen Parvanov and Zhao, Zhijin (2019) A Distance-Type-Insensitive Clustering Approach. Applied Soft Computing, 77. pp. 622-634. ISSN 1568-4946
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Abstract
In this paper, we offer a method aiming to minimise the role of distance metric used in clustering. It is well known that the types of distance metric used in clustering algorithms heavily influence the end results, and also makes the algorithms sensitive to imbalanced attribute scales. To solve these problems, a new clustering algorithm using the per-attribute ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the rankings of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternatives. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.