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

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-05-18 DOI:10.1007/s12652-024-04808-9
Jia Zhao, Zhanfeng Yao, Liujun Qiu, Tanghuai Fan, Ivan Lee
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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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基于多簇合并的密度峰聚类算法及其在用户典型负载模式提取中的应用
密度峰聚类(DPC)算法原理简单、运行高效,在各类数据集上具有良好的聚类效果。但该算法仍存在一些缺陷:(1)由于局部密度和样本相对距离的定义限制,该算法很难找到正确的密度峰;(2)该算法的分配策略鲁棒性差,容易引发其他问题。针对上述不足,我们提出了一种基于多簇合并的密度峰聚类算法(DPC-MM)。针对 DPC 算法难以选取密度峰的问题,定义了一种新的样本相对距离计算方法,使密度峰的选取更加准确。提出了多簇合并的分配策略,以减轻或避免分配误差带来的问题。实验结果表明,DPC-MM 算法可以有效地对任何形状和规模的数据集进行聚类。DPC-MM 算法被应用于用户典型负荷模式的提取,能更准确地对用户负荷进行聚类。提取结果能更好地反映用户的用电习惯。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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