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An Automatic Multi-Objective Clustering Based on Hierarchical Method

In: Recent Developments in Data Science and Business Analytics

Author

Listed:
  • Chao Chen

    (Shandong Normal University)

  • Feng Qi

    (Shandong Normal University)

Abstract

Just optimizing a single objective function or need to know the exact number of clusters in advance is the choice of most clustering methods. However, less knowledge of the data set to be clustered makes it difficult to select the appropriate number of clusters. Motivated by this, we propose an automatic multi-objective clustering based on hierarchical method (AMOH-Cluster), which can not only automatically calculate the optimal number of clusters but also divide all data sets properly based on intra-cluster data compactness and inter-cluster data connectivity. The proposed algorithm has advantages of providing higher clustering accuracy and requiring only a few parameters. As shown in the experiment, the comparison with the known multi-objective clustering algorithms proves that the proposed algorithm provides a solution with higher accuracy and optimal clustering number in various clusters of artificial data sets.

Suggested Citation

  • Chao Chen & Feng Qi, 2018. "An Automatic Multi-Objective Clustering Based on Hierarchical Method," Springer Proceedings in Business and Economics, in: Madjid Tavana & Srikanta Patnaik (ed.), Recent Developments in Data Science and Business Analytics, chapter 0, pages 341-350, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-72745-5_38
    DOI: 10.1007/978-3-319-72745-5_38
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