A Review of Clustering Algorithms Based on Anchor Point Acceleration Mechanism

doi:10.16183/j.cnki.jsjtu.2024.425

Abstract

Abstract:

With the advent of the big data era, clustering algorithms have become pivotal in data mining and machine learning. However, the exponential growth in data size and dimensionality has resulted in escalating time and space complexities for traditional clustering methods, constraining their practical utility. To address these challenges, the anchor point acceleration mechanism has emerged as a potent approach to significantly mitigate computational burdens, thereby augmenting the effectiveness of conventional clustering algorithms for large-scale datasets. This paper provides a comprehensive review of clustering algorithms leveraging the anchor point acceleration mechanism. It explores various techniques such as anchor point generation and the construction of similarity graphs. The discussion encompasses clustering methodologies utilizing fixed anchor point, encompassing spectral clustering, fuzzy spectral clustering, multi-view clustering, and deep clustering algorithms. Additionally, it investigates clustering strategies employing dynamic anchor points, including multi-view and incomplete multi-view clustering algorithms. By synthesizing and analyzing this landscape, this paper identifies current limitations and confronts emerging challenges. It also offers insights into future avenues for advancement, serving as a roadmap for guiding future research and practical applications in the field, fostering continued innovation in clustering algorithms tailored for contemporary data environments.

Key words: clustering, anchors, spectral clustering, fuzzy clustering, multi-view clustering, deep clustering

CLC Number:

TP391

WU Qinting, FENG Yuzhe, PAN Jinyan, ZHANG Haifeng, CAO Chao, GAO Yunlong. A Review of Clustering Algorithms Based on Anchor Point Acceleration Mechanism[J]. Journal of Shanghai Jiao Tong University, 2026, 60(5): 705-724.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Performance comparison of anchor-based and non-anchor-based spectral clustering algorithms

数据集	算法	改进措施	ACC	运行时间/s
USPS	SC^[32]	谱聚类算法(不使用锚点)	0.433	430.350
	CF^[30]	在核空间实现的扩展非负矩阵分解(不使用锚点)	0.301	19.347
	BKM^[30]	快速双边K-means算法(不使用锚点)	0.204	2.623
	KMM^[30]	扩展的K-means算法(不使用锚点)	0.447	10.874
	LSC-R^[31]	使用随机采样来选择地标的SC(不使用锚点)	0.592	7.000
	LSC-K^[31]	使用K-means来选择地标的SC(不使用锚点)	0.668	13.100
	ECCA^[30]	引入相关熵准则	0.466	0.187
	RPSC^[32]	利用BKHK生成两层锚点	0.617	23.260
	FDBC^[43]	联合框架+改进的坐标下降优化算法	0.675	1.256
	FANCEC^[40]	统一的优化框架	0.705	20.154
	FSSC^[31]	结合标签信息	0.729	11.900
Mnist	BKM^[30]	快速双边K-means算法(不使用锚点)	0.134	7.956
	KMM^[30]	扩展的K-means算法(不使用锚点)	0.533	41.045
	LSC-R^[31]	使用随机采样来选择地标的SC(不使用锚点)	0.492	102.900
	LSC-K^[31]	使用K-means来选择地标的SC(不使用锚点)	0.583	447.600
	FCC^[26]	引入相关熵准则	0.362	6.118
	RPSC^[32]	利用BKHK生成两层锚点	0.535	49.930
TDT2	CF^[30]	在核空间实现的扩展非负矩阵分解(不使用锚点)	0.450	129.103
	BKM^[30]	快速双边K-means算法(不使用锚点)	0.383	144.690
	KMM^[30]	扩展的K-means算法(不使用锚点)	0.126	127.588
	FDBC^[43]	联合框架+改进的坐标下降优化算法	0.367	25.329
	ECCA^[30]	引入相关熵准则	0.452	0.726
	FCC^[29]	引入相关熵准则	0.522	62.036
Letter	SC^[32]	谱聚类算法(不使用锚点)	0.236	430.350
	LSC-R^[31]	使用随机采样来选择地标的SC(不使用锚点)	0.254	16.500
	LSC-K^[31]	使用K-means来选择地标的SC(不使用锚点)	0.265	17.900
	RPSC^[32]	利用BKHK生成两层锚点	0.270	14.250
	FSSC^[31]	结合标签信息	0.339	13.900

Tab.1

Tab.2

Fig.5

Tab.3

Fig.6

Fig.7

Tab.4

Tab.5

Performance comparison of multi-view clustering algorithms based on fixed anchor points

数据集	算法	改进措施	ACC	运行时间/s
WebKB	EMKMC-L^[57]	约束谱嵌入矩阵获取聚类标签矩阵+LLM优化	0.917	0.224
	ERMF-AGR^[54]	引入相关熵	0.933	0.535
	EMKMC-M^[57]	约束谱嵌入矩阵获取聚类标签矩阵+快速优化	0.945	0.103
	FMCSE^[58]	将谱嵌入矩阵直接转化为聚类标签矩阵	0.952	0.310
Minist4	EMKMC-L^[57]	约束谱嵌入矩阵获取聚类标签矩阵+LLM优化	0.267	0.596
	FMCSE^[58]	将谱嵌入矩阵直接转化为聚类标签矩阵	0.840	3.797
	EMKMC-M^[57]	约束谱嵌入矩阵获取聚类标签矩阵+快速优化	0.859	0.077
	ERMF-AGR^[54]	引入相关熵	0.893	5.528
Minist	EMKMC-L^[57]	约束谱嵌入矩阵获取聚类标签矩阵+LLM优化	0.116	0.028
	ERMF-AGR^[54]	引入相关熵	0.701	26.184
	EMKMC-M^[57]	约束谱嵌入矩阵获取聚类标签矩阵+快速优化	0.711	0.220
Caltech7	EMKMC-M^[57]	约束谱嵌入矩阵获取聚类标签矩阵+快速优化	0.516	0.147
	EMKMC-L^[57]	约束谱嵌入矩阵获取聚类标签矩阵+LLM优化	0.518	0.373
	ERMF-AGR^[54]	引入相关熵	0.570	0.003
Reuters	EMKMC-L^[57]	约束谱嵌入矩阵获取聚类标签矩阵+LLM优化	0.173	33.782
	EMKMC-M^[57]	约束谱嵌入矩阵获取聚类标签矩阵+快速优化	0.467	24.580
	FMCSE^[58]	将谱嵌入矩阵直接转化为聚类标签矩阵	0.491	87.787
	ERMF-AGR^[54]	引入相关熵	0.559	0.600
	FMGL-MC^[61]	引入低秩约束,确保学习到的共识图具有恰好c个连接分量	0.755	558.260
	TBGL^[55]	基于方差的去相关性锚点选择	0.795	697.430
NUS	EMKMC-L^[57]	约束谱嵌入矩阵获取聚类标签矩阵+LLM优化	0.183	0.490
	EMKMC-M^[57]	约束谱嵌入矩阵获取聚类标签矩阵+快速优化	0.223	0.157
	FMCSE^[58]	将谱嵌入矩阵直接转化为聚类标签矩阵	0.224	2.440
	TBGL^[55]	基于方差的去相关性锚点选择	0.274	552.350

Tab.5

Tab.6

Performance comparison of multi-view clustering algorithms based on dynamic anchor points

数据集	算法	改进措施	改进方面	ACC
Caltech101-20	TAGL-MC^[81]	将多视图数据映射至潜在嵌入空间	锚点质量	0.588
	BIGMC^[91]	构建特殊的锚点来捕捉视图间的共识信息	高效地融合多视图信息	0.611
	MVFCAG^[96]	引入模糊约束直接获得聚类标签	解决采用两阶段方案获取离散聚类标签的问题	0.614
	LAGAR-MC^[87]	构建特殊的锚点来捕捉视图间的共识信息	高效地融合多视图信息	0.633
	FPFAG-MC^[97]	引入正则化项	避免得到平凡解并进一步优化聚类效果	0.655
	2C-MEAL^[80]	使用嵌入学习过滤不良信息	锚点质量	0.664
	PIAL^[82]	将锚点学习与图构造整合至统一的优化框架	锚点质量	0.671
	FMVSC-BAG^[83]	将锚点、图和标签学习集成至统一框架	锚点质量	0.751
CCV	FMVSC-BAG^[83]	将锚点、图和标签学习集成至统一框架	锚点质量	0.237
	FPFAG-MC^[97]	引入正则化项	避免得到平凡解并进一步优化聚类效果	0.240
	PIAL^[82]	将锚点学习与图构造整合至统一的优化框架	锚点质量	0.242
	2C-MEAL^[80]	使用嵌入学习过滤不良信息	锚点质量	0.258
	TAGL-MC^[81]	将多视图数据映射至潜在嵌入空间	锚点质量	0.445
Caltech101-all	FMVSC-BAG^[83]	将锚点、图和标签学习集成至统一框架	锚点质量	0.232
	2C-MEAL^[80]	使用嵌入学习过滤不良信息	锚点质量	0.290
	FPFAG-MC^[97]	引入正则化项	避免得到平凡解并进一步优化聚类效果	0.302
	PIAL^[82]	将锚点学习与图构造整合至统一的优化框架	锚点质量	0.308
	TAGL-MC^[81]	将多视图数据映射至潜在嵌入空间	锚点质量	0.547
SUNRGBD	TAGL-MC^[81]	将多视图数据映射至潜在嵌入空间	锚点质量	0.227
	FPFAG-MC^[97]	引入正则化项	避免得到平凡解并进一步优化聚类效果	0.239
	FMVSC-BAG^[83]	将锚点、图和标签学习集成至统一框架	锚点质量	0.244
	2C-MEAL^[80]	使用嵌入学习过滤不良信息	锚点质量	0.247
	PIAL^[82]	将锚点学习与图构造整合至统一的优化框架	锚点质量	0.252
NUSWIDEOBJ	FMVSC-BAG^[83]	将锚点、图和标签学习集成至统一框架	锚点质量	0.193
	FPFAG-MC^[97]	引入正则化项	避免得到平凡解并进一步优化聚类效果	0.195
	PIAL^[82]	将锚点学习与图构造整合至统一的优化框架	锚点质量	0.202
Minist	EDMC^[85]	充分利用数据集中列的聚类表示空间	探究锚点本身的性质与信息	0.738
	LAGAR-MC^[87]	引入模糊约束直接获得聚类标签	解决采用两阶段方案获取离散聚类标签的问题	0.867
	MVFCAG^[96]	首先融合多视图信息,进而构建统一的锚点图	高效地融合多视图信息	0.975
	FPFAG-MC^[97]	引入正则化项	避免得到平凡解并进一步优化聚类效果	0.988

Tab.6

Tab.7

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数据集	算法	改进措施	ACC	运行时间/s
USPS	KM^[49]	K-means(不使用锚点)	0.630	0.102
	FCM^[49]	模糊C均值(不使用锚点)	0.639	1.193
	GBFC^[[49]	引入正则化项	0.703	1.641
	FFCAG^[50]	引入锚点的先验知识+正则化项	0.802	2.917
TDT2	KM^[47]	K-means(不使用锚点)	0.224	7.582
	FCM^[47]	模糊C均值(不使用锚点)	0.143	393.831
	GBFC^[49]	引入正则化项	0.809	2.172
	FFCAG^[50]	引入锚点的先验知识+正则化项	0.953	68.265
Waveform	KM^[47]	K-means(不使用锚点)	0.511	0.062
	FCM^[47]	模糊C均值(不使用锚点)	0.511	0.065
	AFSEFK^[47]	利用锚点同时学习局部和全局结构	0.571	-
	GBFC^[49]	引入正则化项	0.608	0.118