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Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient

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  • Ziqi Jia
  • Ling Song

Abstract

The k-prototypes algorithm is a hybrid clustering algorithm that can process Categorical Data and Numerical Data. In this study, the method of initial Cluster Center selection was improved and a new Hybrid Dissimilarity Coefficient was proposed. Based on the proposed Hybrid Dissimilarity Coefficient, a weighted k-prototype clustering algorithm based on the hybrid dissimilarity coefficient was proposed (WKPCA). The proposed WKPCA algorithm not only improves the selection of initial Cluster Centers, but also puts a new method to calculate the dissimilarity between data objects and Cluster Centers. The real dataset of UCI was used to test the WKPCA algorithm. Experimental results show that WKPCA algorithm is more efficient and robust than other k-prototypes algorithms.

Suggested Citation

  • Ziqi Jia & Ling Song, 2020. "Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, July.
    • Handle: RePEc:hin:jnlmpe:5143797
    DOI: 10.1155/2020/5143797
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    Cited by:

    1. Jovan Chew & Anurag Sharma & Dhivya Sampath Kumar & Wenjie Zhang & Nandini Anant & Jiaxin Dong, 2024. "Unveiling the Dynamics of Residential Energy Consumption: A Quantitative Study of Demographic and Personality Influences in Singapore Using Machine Learning Approaches," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    2. Aurea Grané & Alpha A. Sow-Barry, 2021. "Visualizing Profiles of Large Datasets of Weighted and Mixed Data," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    3. Konstantinos Gratsos & Stefanos Ougiaroglou & Dionisis Margaris, 2023. "kClusterHub: An AutoML-Driven Tool for Effortless Partition-Based Clustering over Varied Data Types," Future Internet, MDPI, vol. 15(10), pages 1-22, October.

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