A Survey of Constrained Clustering

Date

2016-04-01

Author

Dinler, Derya
Tural, Mustafa Kemal

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Traditional data mining methods for clustering only use unlabeled data objects as input. The aim of such methods is to find a partition of these unlabeled data objects in order to discover the underlying structure of the data. In some cases, there may be some prior knowledge about the data in the form of (a few number of) labels or constraints. Performing traditional clustering methods by ignoring the prior knowledge may result in extracting irrelevant information for the user. Constrained clustering, i.e., clustering with side information or semi-supervised clustering, addresses this problem by incorporating prior knowledge into the clustering process to discover relevant information from the data. In this chapter, a survey of advances in the area of constrained clustering will be presented. Different types of prior knowledge considered in the literature, and clustering approaches that make use of this prior knowledge will be reviewed.

URI

https://link.springer.com/book/10.1007/978-3-319-24211-8#toc
https://hdl.handle.net/11511/72379

Relation

Unsupervised Learning Algorithms

Collections

Department of Industrial Engineering, Book / Book chapter

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Citation Formats

D. Dinler and M. K. Tural, A Survey of Constrained Clustering. 2016, p. 235.