Jia Zhao, Zhanfeng Yao, Liujun Qiu, Tanghuai Fan, Ivan Lee
{"title":"Density peaks clustering algorithm based on multi-cluster merge and its application in the extraction of typical load patterns of users","authors":"Jia Zhao, Zhanfeng Yao, Liujun Qiu, Tanghuai Fan, Ivan Lee","doi":"10.1007/s12652-024-04808-9","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Humanized Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12652-024-04808-9","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
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.
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