Cluster validity methods: part I

By Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis. SGMD, 2002

585 citations recorded by the source corpus.

Clustering is an unsupervised process since there are no predefined classes and no examples that would indicate grouping properties in the data set. The majority of the clustering algorithms behave differently depending on the features of the data set and the initial assumptions for defining groups. Therefore, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. Evaluating and assessing the results of a clustering algorithm is the main subject of cluster validity. In this paper we present a review of the clustering validity and methods. More specifically, Part I of the paper discusses the cluster validity approaches based on external and internal criteria.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3eba6ce49431e41dd3bf6e66ea699b61fb7505b7

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

Citations in the corpus

CUDA-Based Parallelization of Power Iteration Clustering for Large Datasets (2017, methodology): “The criteria adopted for assessing the quality of the yielded clusters partition was based on a cluster quality index [20].”
Selection of K in K-means clustering (2005, methodology): “Monte Carlo techniques, which are associated with the null hypothesis , are used for assessing the clustering results and also for determining the number of clusters [ 24 , 25 ].”
Survey of clustering algorithms (2005, background): “Some more recent discussion can be found in [22], [37], [ 121 ], [180], and [181].”
Cluster validity measurement techniques (2006, background): “Several clustering validity approaches have been developed [2] [3].”
Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns (2013, methodology): “In [26], some of these validation methods are introduced, concluding that they usually work better when dealing with compact clusters.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “index [295,296], root-mean-square standard deviation index [295,296], RS index [297], PBM index [300] and SV index [301].”
A survey of techniques for internet traffic classification using machine learning (2008, background): “(More details can be found in [37] [38] [36] [29].”