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Density peaks clustering algorithm based on multi-cluster merge and its application in the extraction of typical load patterns of users

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Abstract

The density peaks clustering (DPC) algorithm is simple in principle, efficient in operation, and has good clustering effects on various types of datasets. However, this algorithm still has some defects: (1) due to the definition limitations of local density and relative distance of samples, it is difficult for the algorithm to find correct density peaks; (2) the allocation strategy of the algorithm has poor robustness and is prone to cause other problems. In response to solve the above shortcomings, we proposed a density peaks clustering algorithm based on multi-cluster merge (DPC-MM). In view of the difficulty in selecting density peaks of the DPC algorithm, a new method of calculating relative distance of samples was defined to make the density peaks found more accurate. The allocation strategy of multi-cluster merge was proposed to alleviate or avoid problems caused by allocation errors. Experimental results revealed that the DPC-MM algorithm can efficiently perform clustering on datasets of any shape and scale. The DPC-MM algorithm was applied in extraction of typical load patterns of users, and can more accurately perform clustering on user loads. The extraction results can better reflect electricity consumption habits of users.

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Funding

This work is funded by The National Natural Science Foundation of China (CN) (Grant No.: 52069014).

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jia Zhao, Zhanfeng Yao, Liujun Qiu & Tanghuai Fan

  2. Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City, Nanchang Institute of Technology, Nanchang, 330099, China

    Jia Zhao, Zhanfeng Yao, Liujun Qiu & Tanghuai Fan

  3. UniSA STEM, University of South Australia, Adelaide, SA, 5000, Australia

    Ivan Lee

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  1. Jia Zhao
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  2. Zhanfeng Yao
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  3. Liujun Qiu
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  4. Tanghuai Fan
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  5. Ivan Lee
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Correspondence to Jia Zhao.

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Zhao, J., Yao, Z., Qiu, L. et al. Density peaks clustering algorithm based on multi-cluster merge and its application in the extraction of typical load patterns of users. J Ambient Intell Human Comput 15, 3193–3209 (2024). https://doi.org/10.1007/s12652-024-04808-9

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  • DOI: https://doi.org/10.1007/s12652-024-04808-9

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