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A New K-Multiple-Means Clustering Method

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Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13369))

Abstract

In the field of clustering, non-spherical data clustering is a relatively complex case. To satisfy the practical application, the solution should be able to capture non-convex patterns in data sets with high performance. At present, the multi-prototype method can meet the former requirement, but the time cost is still high. This paper proposes a new multi-prototype extension of the K-multiple-means type algorithm, which aims to further reduce the computation time in processing non-spherical data sets with a concise principle while maintaining close performance. Compared with other methods, the method still adopts the idea of multiple prototypes and uses agglomerative strategies in the phase of class cluster connection. However, to reduce the amount of data involved in the computation and the interference of incorrect partition, the subclass data of the first partition is filtered. In addition, the agglomeration is divided into two stages: the agglomeration between prototypes and the agglomeration between clusters, and two agglomeration modes are provided to deal with different clustering tasks. Before updating the means, the filtered data needs a quadratic partition. Experimental results show that compared with the state-of-the-art approaches, the proposed method is still effective with lower time complexity in both synthetic and real-world data sets.

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Notes

  1. 1.

    https://github.com/CHLWR/KDD2019_K-Multiple-Means.

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences (Wuhan), Wuhan, China

    Jingyuan Zhang

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  1. Jingyuan Zhang
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Correspondence to Jingyuan Zhang .

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Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Zhang, J. (2022). A New K-Multiple-Means Clustering Method. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_50

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  • DOI: https://doi.org/10.1007/978-3-031-10986-7_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10985-0

  • Online ISBN: 978-3-031-10986-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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