[1]吕莉,陈威,肖人彬,等.面向密度分布不均数据的加权逆近邻密度峰值聚类算法[J].智能系统学报,2024,19(1):165-175.[doi:10.11992/tis.202212015]
LYU Li,CHEN Wei,XIAO Renbin,et al.Density peak clustering algorithm based on weighted reverse nearest neighbor for uneven density datasets[J].CAAI Transactions on Intelligent Systems,2024,19(1):165-175.[doi:10.11992/tis.202212015]
点击复制
LYU Li,CHEN Wei,XIAO Renbin,et al.Density peak clustering algorithm based on weighted reverse nearest neighbor for uneven density datasets[J].CAAI Transactions on Intelligent Systems,2024,19(1):165-175.[doi:10.11992/tis.202212015]
面向密度分布不均数据的加权逆近邻密度峰值聚类算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第1期
页码:
165-175
栏目:
学术论文—人工智能基础
出版日期:
2024-01-05
- Title:
- Density peak clustering algorithm based on weighted reverse nearest neighbor for uneven density datasets
- Keywords:
- density peak clustering; uneven density distribution; reverse nearest neighbor; shared reverse nearest neighbor; sample similarity; local density; distribution strategy; data mining
- 分类号:
- TP301
- 文献标志码:
- 2023-08-02
- 摘要:
- 针对密度分布不均数据，密度峰值聚类算法易忽略类簇间样本的疏密差异，导致误选类簇中心；分配策略易将稀疏区域的样本误分到密集区域，导致聚类效果不佳的问题，本文提出一种面向密度分布不均数据的加权逆近邻密度峰值聚类算法。该算法首先在局部密度公式中引入基于sigmoid函数的权重系数，增加稀疏区域样本的权重，结合逆近邻思想，重新定义了样本的局部密度，有效提升类簇中心的识别率；其次，引入改进的样本相似度策略，利用样本间的逆近邻及共享逆近邻信息，使得同一类簇样本间具有较高的相似度，可有效改善稀疏区域样本分配错误的问题。在密度分布不均、复杂形态和UCI数据集上的对比实验表明，本文算法的聚类效果优于IDPC-FA、FNDPC、FKNN-DPC、DPC和DPCSA算法。
- Abstract:
- For data with uneven density distribution, the density peak clustering algorithm disregards the sparsity difference among intercluster samples, causing an inaccurate selection of the cluster center. Moreover, the allocation strategy easily divides the samples in sparse areas into dense areas by mistake, leading to a poor clustering effect. Therefore, the density peak clustering algorithm based on the weighted reverse nearest neighbor (DPC-WR) against datasets with uneven density distribution is proposed in this paper. First, the weight coefficient based on the sigmoid function is introduced to the local density formula to increase the weight of samples in sparse areas. Combined with the concept of reverse nearest neighbor, the local density of samples is then redesigned to improve the recognition rate of cluster centers effectively. Second, an improved sample similarity strategy is introduced, which utilizes reverse nearest neighbors and shares this neighbor’s information between samples to increase the similarity of samples in the same cluster. This effectively solves the problem of sample allocation error in sparse areas. Experiments on uneven density distribution, complex morphology, and UCI datasets show that the clustering effect of the DPC-WR algorithm outperforms that of IDPC-FA, FNDPC, FKNN-DPC, DPC, and DPCSA algorithms.
- 参考文献/References:
-
[1] SZALONTAI B, DEBRECZENY M, FINTOR K, et al. SVD-clustering, a general image-analyzing method explained and demonstrated on model and Raman micro-spectroscopic maps[J]. Scientific reports, 2020, 10: 4238.
[2] 程鹏宇, 赵嘉, 韩龙哲, 等. 双向多尺度LSTM的短时温度预测[J]. 江西师范大学学报(自然科学版), 2022, 46(2): 134–139
CHENG Pengyu, ZHAO Jia, HAN Longzhe, et al. The short-term temperature prediction based on bidirectional multi-scale LSTM[J]. Journal of Jiangxi normal university (natural science edition), 2022, 46(2): 134–139
[3] LI Chunlin, BAI Jingpan. Automatic content extraction and time-aware topic clustering for large-scale social network on cloud platform[J]. The journal of supercomputing, 2019, 75(5): 2890–2924.
[4] MIRZAL A. Statistical analysis of microarray data clustering using NMF, spectral clustering, kmeans, and GMM[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2022, 19(2): 1173–1192.
[5] ZHAO Yuan, FANG Zhaoyu, LIN Cuixiang, et al. RFCell: a gene selection approach for scRNA-seq clustering based on permutation and random forest[J]. Frontiers in genetics, 2021, 12: 665843.
[6] TAVALLALI P, TAVALLALI P, SINGHAL M. K-means tree: an optimal clustering tree for unsupervised learning[J]. The journal of supercomputing, 2021, 77(5): 5239–5266.
[7] CHU Zhenyue, WANG Weifeng, LI Bangzhun, et al. An operation health status monitoring algorithm of special transformers based on BIRCH and Gaussian cloud methods[J]. Energy reports, 2021, 7: 253–260.
[8] BUREVA V, POPOV S, TRANEVA V, et al. Generalized net model of cluster analysis using CLIQUE: clustering in quest[J]. International journal bioautomation, 2019, 23(2): 131–138.
[9] REN Jie, WANG Zulin, XU Mai, et al. An EM-based user clustering method in non-orthogonal multiple access[J]. IEEE transactions on communications, 2019, 67(12): 8422–8434.
[10] ZHU Qidan, TANG Xiangmeng, ELAHI A. Application of the novel harmony search optimization algorithm for DBSCAN clustering[J]. Expert systems with applications, 2021, 178: 115054.
[11] ZHANG En, LI Huimin, HUANG Yuchen, et al. Practical multi-party private collaborative k-means clustering[J]. Neurocomputing, 2022, 467: 256–265.
[12] ZHANG Tian, RAMAKRISHNAN R, LIVNY M. Birch[J]. ACM SIGMOD record, 1996, 25(2): 103–114.
[13] AGRAWAL R, GEHRKE J, GUNOPULOS D, et al. Automatic subspace clustering of high dimensional data for data mining applications[C]//Proceedings of the 1998 ACM SIGMOD international conference on Management of data. Seattle: ACM, 1998: 94?105.
[14] DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the royal statistical society:series B (methodological), 1977, 39(1): 1–22.
[15] ESTER M. A density-based Algorithm for discovering clusters in large spatial databases with noise[C]//KDD ’96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Oregon: AAAI Press, 1996: 226?231.
[16] RODRIGUEZ A, LAIO A. Machine learning. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492–1496.
[17] 吕莉, 朱梅子, 康平, 等. 二阶K近邻和多簇合并的密度峰值聚类算法[J/OL]. 吉林大学学报(工学版). (2023?01?31)[2023?07?13]. https://kns.cnki.net/kcms/detail/22.1341.t.20230131.1100. 005. html.
LYU Li, ZHU Meizi, KANG Ping, et al. Density peaks clustering with second-order K-nearest neighbors and multi-cluster merging[J/OL]. Journal of Jilin University (engineering and technology edition). (2023?01?31) [2023?07?13]. https://kns.cnki.net/kcms/detail/22.1341.t.20230131.1100.005.html.
[18] SUN Lin, QIN Xiaoying, DING Weiping, et al. Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy[J]. Neurocomputing, 2022, 473: 159–181.
[19] 赵嘉, 陈磊, 吴润秀, 等. K近邻和加权相似性的密度峰值聚类算法[J]. 控制理论与应用, 2022, 39(12): 2349–2357
ZHAO Jia, CHEN Lei, WU Runxiu, et al. Density peaks clustering algorithm with K-nearest neighbors and weighted similarity[J]. Control theory & applications, 2022, 39(12): 2349–2357
[20] 吴润秀, 尹士豪, 赵嘉, 等. 基于相对密度估计和多簇合并的密度峰值聚类算法[J]. 控制与决策, 2023, 38(4): 1047–1055
WU Runxiu, YIN Shihao, ZHAO Jia, et al. Density peaks clustering based on relative density estimating and multi cluster merging[J]. Control and decision, 2023, 38(4): 1047–1055
[21] DING Jiajun, HE Xiongxiong, YUAN Junqing, et al. Community detection by propagating the label of center[J]. Physica A:statistical mechanics and its applications, 2018, 503: 675–686.
[22] 赵嘉, 王刚, 吕莉, 等. 面向流形数据的测地距离与余弦互逆近邻密度峰值聚类算法[J]. 电子学报, 2022, 50(11): 2730–2737
ZHAO Jia, WANG Gang, LYU Li, et al. Density peaks clustering algorithm based on geodesic distance and cosine mutual reverse nearest neighbors for manifold datasets[J]. Acta electronica sinica, 2022, 50(11): 2730–2737
[23] BRYANT A, CIOS K. RNN-DBSCAN: a density-based clustering algorithm using reverse nearest neighbor density estimates[J]. IEEE transactions on knowledge and data engineering, 2017, 30(6): 1109–1121.
[24] 陈磊, 吴润秀, 李沛武, 等. 加权K近邻和多簇合并的密度峰值聚类算法[J]. 计算机科学与探索, 2022, 16(9): 2163–2176
CHEN Lei, WU Runxiu, LI Peiwu, et al. Weighted K-nearest neighbors and multi-cluster merge density peaks clustering algorithm[J]. Journal of frontiers of computer science and technology, 2022, 16(9): 2163–2176
[25] ZHAO Jia, TANG Jingjing, SHI Aiye, et al. Improved density peaks clustering based on firefly algorithm[J]. International journal of bio-inspired computation, 2020, 15(1): 24.
[26] DU Mingjing, DING Shifei, XUE Yu. A robust density peaks clustering algorithm using fuzzy neighborhood[J]. International journal of machine learning and cybernetics, 2018, 9(7): 1131–1140.
[27] YU Donghua, LIU Guojun, GUO Maozu, et al. Density peaks clustering based on weighted local density sequence and nearest neighbor assignment[J]. IEEE access, 2019, 7: 34301–34317.
[28] VINH N X, EPPS J, BAILEY J. Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance[J]. Journal of machine learning research, 2010, 11: 2837–2854.
[29] FOWLKES E B, MALLOWS C L. A method for comparing two hierarchical clusterings[J]. Journal of the American statistical association, 1983, 78(383): 553–569.
[30] ZIMMERMAN D W, ZUMBO B D. Relative power of the wilcoxon test, the Friedman test, and repeated-measures ANOVA on ranks[J]. The journal of experimental education, 1993, 62(1): 75–86.
[31] 肖人彬. 面向复杂系统的群集智能[M]. 北京: 科学出版社, 2013.
[32] 肖人彬, 冯振辉, 王甲海. 群体智能的概念辨析与研究进展及应用分析[J]. 南昌工程学院学报, 2022, 41(1): 1–21
XIAO Renbin, FENG Zhenhui, WANG Jiahai. Collective intelligence: conception, research progresses and application analyses[J]. Journal of Nanchang institute of technology, 2022, 41(1): 1–21
[33] 张曦, 李璠, 付雪峰, 等. 随机学习萤火虫算法优化的模糊软子空间聚类算法[J]. 江西师范大学学报(自然科学版), 2021, 45(2): 137–144
ZHANG Xi, LI Fan, FU Xuefeng, et al. The fuzzy soft subspace clustering algorithm optimized by random learning firefly algorithm[J]. Journal of Jiangxi normal university (natural science edition), 2021, 45(2): 137–144
[34] 肖人彬, 陈峙臻. 从群智能优化到群智能进化[J]. 南昌工程学院学报, 2023, 42(1): 1–10
XIAO Renbin, CHEN Zhizhen. From swarm intelligence optimization to swarm intelligence evolution[J]. Journal of Nanchang institute of technology, 2023, 42(1): 1–10
[35] 赵嘉, 谢智峰, 吕莉, 等. 深度学习萤火虫算法[J]. 电子学报, 2018, 46(11): 2633–2641
ZHAO Jia, XIE Zhifeng, LYU Li, et al. Firefly algorithm with deep learning[J]. Acta electronica sinica, 2018, 46(11): 2633–2641
- 相似文献/References:
-
[1]胡峰,李路正,代劲,等.结合聚类边界采样的主动学习[J].智能系统学报,2024,19(2):482.[doi:10.11992/tis.202205020]
HU Feng,LI Luzheng,DAI Jin,et al.Active learning combined with clustering boundary sampling[J].CAAI Transactions on Intelligent Systems,2024,19():482.[doi:10.11992/tis.202205020]
备注/Memo
收稿日期:2022-12-13。
基金项目:国家自然科学基金项目(62066030); 江西省重点研发计划项目(20192BBE50076，20203BBGL73225); 江西省教育厅科技项目(GJJ190958).
作者简介:吕莉，教授，博士，主要研究方向为智能计算与计算智能、目标跟踪与检测、大数据与人工智能。 主持国家自然科学基金项目2项，发表学术论文80余篇。E-mail：lvli623@163.com;陈威，硕士研究生， 主要研究方向为数据挖掘。E-mail：chenwei9801@163.com;肖人彬，教授，博士生导师，主要研究方向为群体智能、大规模个性化定制、复杂系统与复杂性科学。主持国家自然科学基金项目11项，主持获得教育部自然科学奖1项和湖北省自然科学奖及科技进步奖4 项，发表学术论文300余篇。出版学术专著和教 材10余部。E-mail：rbxiao@hust.edu.cn
通讯作者:吕莉. E-mail：lvli623@163.com
更新日期/Last Update:
1900-01-01