Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb


Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_943

Clustering is a type of  unsupervised learning in which the goal is to partition a set of  examples into groups called clusters. Intuitively, the examples within a cluster are more similar to each other than to examples from other clusters. In order to measure the similarity between examples, clustering algorithms use various distortion or  distance measures. There are two major types clustering approaches: generative and discriminative. The former assumes a parametric form of the data and tries to find the model parameters that maximize the probability that the data was generated by the chosen model. The latter represents graph-theoretic approaches that compute a similarity matrix defined over the input data.


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© Springer Science+Business Media New York 2017