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摘要:
In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.
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篇名 Multi-view Clustering: A Survey
来源期刊 大数据挖掘与分析(英文) 学科 工学
关键词 MULTI-VIEW CLUSTERING CO-TRAINING multi-kernel LEARNING graph CLUSTERING SUBSPACE CLUSTERING SUBSPACE LEARNING non-negative matrix factorization MULTI-TASK LEARNING
年,卷(期) 2018,(2) 所属期刊栏目
研究方向 页码范围 83-107
页数 25页 分类号 TP311.13
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
MULTI-VIEW
CLUSTERING
CO-TRAINING
multi-kernel
LEARNING
graph
CLUSTERING
SUBSPACE
CLUSTERING
SUBSPACE
LEARNING
non-negative
matrix
factorization
MULTI-TASK
LEARNING
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
大数据挖掘与分析(英文)
季刊
2096-0654
10-1514/G2
出版文献量(篇)
91
总下载数(次)
3
总被引数(次)
0
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