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Adaptive fuzzy c-means clustering algorithm for interval data type based on interval-dividing technique

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Abstract

Clustering for symbolic data type is a necessary process in many scientific disciplines, and the fuzzy c-means clustering for interval data type (IFCM) is one of the most popular algorithms. This paper presents an adaptive fuzzy c-means clustering algorithm for interval-valued data based on interval-dividing technique. This method gives a fuzzy partition and a prototype for each fuzzy cluster by optimizing an objective function. And the adaptive distance between the pattern and its cluster center varies with each algorithm iteration and may be either different from one cluster to another or the same for all clusters. The novel part of this approach is that it takes into account every point in both intervals when computing the distance between the cluster and its representative. Experiments are conducted on synthetic data sets and a real data set. To compare the comprehensive performance of the proposed method with other four existing methods, the corrected rand index, the value of objective function and iterations are introduced as the evaluation criterion. Clustering results demonstrate that the algorithm proposed in this paper has remarkable advantages.

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Acknowledgements

The authors would like to thank Prof. Bo Li for his proof reading and the reviewers for their valuable time. This research is supported by the National Science Foundation (91338115), National S/T Major Project (2015ZX03002006) and the 111 Project (B08038).

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Authors and Affiliations

  1. School of Telecommunications Engineering, Xidian University, 117 Room, Main Building, No. 2 Taibai South Road, Xi’an, 710071, China

    Chaozheng Bao, Hongming Peng, Di He & Junning Wang

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  1. Chaozheng Bao
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  2. Hongming Peng
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  3. Di He
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  4. Junning Wang
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Correspondence to Chaozheng Bao.

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Bao, C., Peng, H., He, D. et al. Adaptive fuzzy c-means clustering algorithm for interval data type based on interval-dividing technique. Pattern Anal Applic 21, 803–812 (2018). https://doi.org/10.1007/s10044-017-0663-2

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  • DOI: https://doi.org/10.1007/s10044-017-0663-2

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