Skip to main content
SpringerLink
Log in

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

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.?1
Fig.?2
Fig.?3
Fig.?4
Fig.?5
Fig.?6
Fig.?7
Fig.?8

Similar content being viewed by others

References

  • Cai X, Geng S, Di Wu et al (2019) A unified heuristic bat algorithm to optimize the LEACH protocol. Concurr Comput Pract Exp 32(9):e5619

    Article  Google Scholar 

  • Cai X, Zhaoming Hu, Chen J (2020) A many-objective optimization recommendation algorithm based on knowledge mining. Inf Sci 537:148?161

    Article  MathSciNet  Google Scholar 

  • Chang H, Yeung DY (2008) Robust path-based spectral clustering. Pattern Recogn 41(1):191?203

    Article  Google Scholar 

  • Charytanowicz M, Niewczas J, Kulczycki P et al (2010) Complete gradient clustering algorithm for features analysis of x-ray images. Springer, Berlin

    Book  Google Scholar 

  • Cui Z, Zhang J, Wang Y et al (2019) A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci China Inf Sci 62(7):070212

    Article  Google Scholar 

  • Dias DB, Madeo RCB, Rocha T et al (2009) Hand movement recognition for brazilian sign language: a study using distance-based neural networks. Proceedings of the International Joint Conference on neural networks, 697?704

  • Du M, Ding S, Xue Y (2018) A robust density peaks clustering algorithm using fuzzy neighborhood. Int J Mach Learn Cybern 9(7):1131?1140

    Article  Google Scholar 

  • Ester M, Kriegel HP, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. 96(34): 226?231

  • Fan T, Yao Z, Han L et al (2021) Density peaks clustering based on K-nearest neighbors sharing. Concurr Comput Pract Exp 33(5):e5993

    Article  Google Scholar 

  • Fowlkes EB, Mallows CL (1983) A Method for comparing two hierarchical clusterings. J Am Stat Assoc 78(383):553?569

    Article  Google Scholar 

  • Fu L, Medico E (2007) FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformatics 8(1):3

    Article  Google Scholar 

  • Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Discov Data 1(1):1?30

    Article  Google Scholar 

  • He Z, Kang P, Li Q et al (2023) Firefly algorithm with combination of multi-strategies. J Nanchang Inst Technol 42(1):80?87

    Google Scholar 

  • Heimerl F, John M, Han Q et al (2016) DocuCompass: Effective exploration of document landscapes. Proceedings of 2016 IEEE Conference on Visual Analytics Science and Technology, 11?20

  • Henninger J, Santoso B, Hans S et al (2017) Clonal fate mapping quantifies the number of haematopoietic stem cells that arise during development. Nat Cell Biol 19(1):17?27

    Article  Google Scholar 

  • Jain AK, Law MH (2005) Data clustering: a user?s dilemma. In: Proceedings of the International conference on pattern recognition and machine intelligence, 1?10

  • Jia H, He G, Fang C et al (2007) Load Forecasting by Multi-Hierarchy clustering combining hierarchy clustering with approaching algorithm in two directions. Power System Technol 31(23):33?36

    Google Scholar 

  • Jia S, Tang G, Zhu J et al (2015) A novel ranking-based clustering approach for hyperspectral band selection. IEEE Trans Geosci Remote Sens 54(1):88?102

    Article  Google Scholar 

  • Lai Z, Runxiu W, Li Q et al (2023) Multi-objective firefly algorithm for group learning. J Nanchang Inst Technol 42(3):73?81

    Google Scholar 

  • Li C, Zhao J, Han L et al (2023) The short-time temperature prediction for multi-channel CNN?BiLSTM. J Jiangxi Normal Univ (Nat Sci) 47(3):325?330

    Google Scholar 

  • Lichman M (2013) UCI machine learning repository. University of California, Irvine. http://archive.ics.uci.edu/ml

  • Lv L, Zhao J, Wang J et al (2019) Multi-objective firefly algorithm based on compensation factor and elite learning. Futur Gener Comput Syst 91:37?47

    Article  Google Scholar 

  • Nizar AH, Dong ZY, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3):946?955

    Article  Google Scholar 

  • Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492?1496

    Article  Google Scholar 

  • Sigillito VG, Wing SP, Hutton LV et al (1989) Classification of radar returns from the ionosphere using neural networks. J Hopkins APL Tech Dig 10(3):262?266

    Google Scholar 

  • Street WN, Wolberg WH, Mangasarian OL (1992) Nuclear feature extraction for breast tumor diagnosis. In: Proceedings of the International Society for Optics and Photonics, 861?871

  • Veenman CJ, Reinders MJT, Backer E (2002) A maximum variance cluster algorithm. IEEE Trans Pattern Anal Mach Intell 24(9):1273?1280

    Article  Google Scholar 

  • Vesanto J (1999) SOM-based data visualization methods. Intell Data Anal 3(2):111?126

    Article  Google Scholar 

  • Vinh N, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res 11(1):2837?2854

    MathSciNet  Google Scholar 

  • Wang P, Huang J, Cui Z et al (2019) A Gaussian error correction multi-objective positioning model with NSGA-II. Concurr Comput Pract Exp 32(5):e5464

    Article  Google Scholar 

  • Wang B, Zhang J, Ding F et al (2017) Multi-document news summarization via paragraph embedding and density peak clustering. In: Proceedings of 2017 International Conference on Asian Language Processing, 260?263

  • Xie J, Gao H, Xie W et al (2016) Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors. Inf Sci 354:19?40

    Article  Google Scholar 

  • Yang HT, Chen SC, Peng PC (2005) Genetic k-means-algorithm-based classification of direct load-control curves. IEE Proc Gener Transm Distrib 152(4):489?495

    Article  Google Scholar 

  • Yu D, Liu G, Guo M et al (2019) Density peaks clustering based on weighted local density sequence and nearest neighbor assignment. IEEE Access 7:34301?34317

    Article  Google Scholar 

  • Zhao J, Tang J, Fan T, Li C, Lizhong Xu (2020a) Density peaks clustering based on circular partition and grids similarity. Concurr Comput Pract Exp 32(7):e5567

    Article  Google Scholar 

  • Zhao J, Tang J, Shi A, Fan T, Lizhong Xu (2020b) Improved density peaks clustering based on firefly algorithm. Int J Bio-Inspir Comput 15(1):24?42

    Article  Google Scholar 

  • Zhao J, Yao Z, Lv L et al (2021) Density peaks clustering based on mutual neighbor degree. Control Decis 36(3):543?552

    Google Scholar 

  • Zhao J, Wang G, Lv L et al (2022) Density peaks clustering algorithm based on geodesic distance and cosine mutual reverse nearest neighbors for manifold datasets. Acta Electron Sin 50(11):2730?2737

    Google Scholar 

  • Zhao J, Chen L, Runxiu W et al (2022) Density peaks clustering algorithm with K-nearest neighbors and weighted similarity. Control Theor Appl 39(12):2349?2357

    Google Scholar 

  • Zhao J, Chen D, Xiao R et al (2022c) Multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity. Appl Soft Comput 123:108938

    Article  Google Scholar 

  • Zhao J, Wang G, Pan J-S, Fan T, Lee I (2023) Density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets. Pattern Recogn 139:109406

    Article  Google Scholar 

Download references

Funding

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

Author information

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

Authors
  1. Jia Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhanfeng Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liujun Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tanghuai Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ivan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jia Zhao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 (2024). https://doi.org/10.1007/s12652-024-04808-9

Download citation

  • Received:

  • Accepted:

  • Published:

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

Keywords

Search

Navigation