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A novel DBSCAN with entropy and probability for mixed data

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Abstract

In big data situation, to detect clusters of different size and shape is a challenging and imperative task. Density based clustering approaches have been widely used in many areas of science due to its simplicity and the ability to detect clusters of different sizes and shapes over the last several years. With diverse conversion on categorical data, a modified version of the DBSCAN algorithm is proposed to cluster mixed data, noted as density based clustering algorithm for mixed data with integration of entropy and probability distribution (EPDCA). Optional and various conversions are provided for clustering process with adaptability. Some benchmark data sets from UCI have been selected for testing the capability and validity of EPDCA. It was shown that the clustering results of EPDCA are considerably improved, especially on automatically number of clusters formed, noise discovery and time elapsed to form clusters.

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Acknowledgements

The authors are very grateful to the editors and reviewers for their valuable comments and suggestions. This work was supported in part by the Major Projects of the National Social Science Fund of China (Grant Nos. 16ZDA045 and 15ZDB168), National Natural Science Foundation of China (Grant Nos. 71603197, 71371148, and 91024020), Junior Fellowships for CAST Advanced Innovation Think-tank Program (DXB-ZKQN-2016-013), China Postdoctoral Science Foundation Funded Project (2016M592403).

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Authors and Affiliations

  1. Wuhan University of Technology, Wuhan, Hubei, China

    Xingxing Liu, Qing Yang & Ling He

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  1. Xingxing Liu
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  2. Qing Yang
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  3. Ling He
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Correspondence to Qing Yang.

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Liu, X., Yang, Q. & He, L. A novel DBSCAN with entropy and probability for mixed data. Cluster Comput 20, 1313–1323 (2017). https://doi.org/10.1007/s10586-017-0818-3

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  • DOI: https://doi.org/10.1007/s10586-017-0818-3

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