Data Clustering: 50 Years Beyond K-means View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008-01-01

AUTHORS

Anil K. Jain

ABSTRACT

The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories. More... »

PAGES

3-4

Book

TITLE

Machine Learning and Knowledge Discovery in Databases

ISBN

978-3-540-87478-2
978-3-540-87479-9

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-87479-9_3

DOI

http://dx.doi.org/10.1007/978-3-540-87479-9_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011801577


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