A Comprehensive Survey of Clustering Algorithms

By Dongkuan Xu, Ying-jie Tian. Annals of Data Science, 2015

1,804 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/966b08ae8efc07838c02fb5005bcc173f978b51d

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Citations in the corpus

Big data for cyber physical systems in industry 4.0: a survey (2019, background): “There are two general models: the statistical model (Xu and Tian 2015) and the neural network model (Du 2010).”
Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, methodology): “This method has wide applicability, and the relationship between clusters is easy to detect; however, the time complexity is high [75].”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “The seven commonly used evaluation indices in clustering are listed in Table 3 (Xu and Tian 2015).”
Search Result Clustering in Collaborative Sound Collections (2020, background): “Among the different approaches for clustering [12, 44], in the context of multimedia documents, density-based algorithms such as graph-based clustering methods are particularly well designed for dealing with computational efficiency and the heterogeneous aspect of the data [32].”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “In such a situation unsupervised algorithms like self organized maps (SOM) [133] and clustering analysis [134] (k-mean, t-SNE [135]) have better utility.”
Meta-Learning to Cluster (2019, methodology): “Clustering is one of the most ubiquitous techniques in data analysis that has important applications in numerous domains that extend far beyond machine learning [Xu et al., 2003, Cai et al., 2004, Tung et al., 2010, Xu and Tian, 2015].”
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, methodology): “K-means [90], K-medoids [107], single linkage [131], complete linkage [132], agglomerative clustering, bottom-up BOTS [118], DBSCAN, OPTICS, Gaussian Mixture Model [148], are the popular concepts of clustering algorithms.”
Lost in Translation, Found in Summary: Summary-Driven Supervision for Cross-Language Code Retrieval (2026, unknown): “On the other hand, meth-ods that can enforce such pairwise similarity constraints [61, 62], such as HCA [7], tend to be computationally expensive and do not scale to large embedding sets due to the quadratic cost of pairwise distance computation.”