Survey of clustering algorithms

By Rui Xu, Donald C. Wunsch II. 2005

6,558 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Novel Class Discovery without Forgetting (2022, background): “Similar instances are grouped together by clustering algorithms [56,14], but they cannot make use of labeled instances from a disjoint set of classes.”
Profiling relational data: a survey (2015, methodology): “We refer the reader to surveys by Jain et al. [78] and Xu and Wunsch II [137] for more details on clustering algorithms for relational data.”
A review of clustering techniques and developments (2017, methodology): “Xu and Wunsch [50, 51] described clustering in the perspective of probability that data objects are drawn from a specific probability distribution and the overall distribution of the data is assumed to be a mixture of several distributions [53].”
A random walk in the literature on criminality: A partial and critical view on some statistical analyses and modelling approaches (2010, neutral): “mostly studied by the community of machine learning research [6, 40, 57, 106].”
Image Clustering Using Local Discriminant Models and Global Integration (2010, methodology): “As suggested in [41], for all the clustering algorithms, we independently repeat the experiments for 20 times with random initializations to reduce”
Biclustering in data mining (2008, background): “A recent survey on clustering methods can be found in [12].”
A survey of techniques for internet traffic classification using machine learning (2008, background): “(More details can be found in [37] [38] [36] [29].”
Multi-view clustering: A survey (2018, neutral): “Clustering has emerged as a powerful alternative learning tool for exploring the underlying structure of data[5, 6], especially in the era of big data[7].”
A survey of kernel and spectral methods for clustering (2008, methodology): “Clustering methods [1] [2] have been addressed in many contexts and disciplines such as data mining, document retrieval, image segmentation and pattern classification.”
A Survey of Evolutionary Algorithms for Clustering (2009, background): “data objects and disclosed clusters [132].”
Relative clustering validity criteria: A comparative overview (2010, background): “see refs [2,4,5])—but solving a clustering problem may also help solving other related problems, such as pattern classification and rule extraction from data [6].”