An Overview on Clustering Methods

By T. Madhulatha. arXiv.org, 2012

797 citations recorded by the source corpus.

Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e1444961fff98048b43bb3212d6633ee8bc9ecc4

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Citations in the corpus

Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “Most of partitional algorithms such as K-means (Forgy 1965), EM (Redner and Walker 1984) need to specify the number of clusters K beforehand, but K is usually unknown in practice (Omran et al. 2007).”
A Systematic Review on Educational Data Mining (2017, background): “In statistical notation, “clustering is the most important unsupervised learning algorithm” [22].”
Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm (2015, background): “In k -means clustering, it partitions a collection of data into a k number group of data 11,12 .”