Multiple clusterings: Recent advances and perspectives

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Guoxian Yu , Liangrui Ren , Jun Wang , Carlotta Domeniconi , Xiangliang Zhang
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引用次数: 0

Abstract

Clustering is a fundamental data exploration technique to discover hidden grouping structure of data. With the proliferation of big data, and the increase of volume and variety, the complexity of data multiplicity is increasing as well. Traditional clustering methods can provide only a single clustering result, which restricts data exploration to one single possible partition. In contrast, multiple clustering can simultaneously or sequentially uncover multiple non-redundant and distinct clustering solutions, which can reveal multiple interesting hidden structures of the data from different perspectives. For these reasons, multiple clustering has become a popular and promising field of study. In this survey, we have conducted a systematic review of the existing multiple clustering methods. Specifically, we categorize existing approaches according to four different perspectives (i.e., multiple clustering in the original space, in subspaces and on multi-view data, and multiple co-clustering). We summarize the key ideas underlying the techniques and their objective functions, and discuss the advantages and disadvantages of each. In addition, we built a repository of multiple clustering resources (i.e., benchmark datasets and codes). Finally, we discuss the key open issues for future investigation.

多重聚类:最新进展与展望
聚类是一种基本的数据探索技术,用于发现数据中隐藏的分组结构。随着大数据的激增、数量和种类的增加,数据多元性的复杂性也在增加。传统的聚类方法只能提供单一的聚类结果,这就把数据探索限制在单一可能的分区上。相比之下,多重聚类可以同时或依次发现多个非冗余且截然不同的聚类方案,从而从不同角度揭示数据中多个有趣的隐藏结构。因此,多重聚类已成为一个热门且前景广阔的研究领域。在本调查中,我们对现有的多重聚类方法进行了系统回顾。具体来说,我们按照四个不同的视角(即原始空间中的多重聚类、子空间中的多重聚类、多视角数据中的多重聚类以及多重协同聚类)对现有方法进行了分类。我们总结了这些技术及其目标函数的主要思想,并讨论了每种技术的优缺点。此外,我们还建立了多个聚类资源库(即基准数据集和代码)。最后,我们讨论了未来研究的关键问题。
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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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