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Clustering Analysis and Algorithms

Clustering Analysis and Algorithms

Xiangji Huang
Copyright: © 2005 |Pages: 6
ISBN13: 9781591405573|ISBN10: 1591405572|EISBN13: 9781591405597
DOI: 10.4018/978-1-59140-557-3.ch031
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MLA

Huang, Xiangji. "Clustering Analysis and Algorithms." Encyclopedia of Data Warehousing and Mining, edited by John Wang, IGI Global, 2005, pp. 159-164. https://doi.org/10.4018/978-1-59140-557-3.ch031

APA

Huang, X. (2005). Clustering Analysis and Algorithms. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining (pp. 159-164). IGI Global. https://doi.org/10.4018/978-1-59140-557-3.ch031

Chicago

Huang, Xiangji. "Clustering Analysis and Algorithms." In Encyclopedia of Data Warehousing and Mining, edited by John Wang, 159-164. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-557-3.ch031

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Abstract

Clustering is the process of grouping a collection of objects (usually represented as points in a multidimensional space) into classes of similar objects. Cluster analysis is a very important tool in data analysis. It is a set of methodologies for automatic classification of a collection of patterns into clusters based on similarity. Intuitively, patterns within the same cluster are more similar to each other than patterns belonging to a different cluster. It is important to understand the difference between clustering (unsupervised classification) and supervised classification.

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