Exact algorithms for size constrained clustering

dc.contributor.author	Jianyi Lin	*
dc.date.accessioned	2021-02-11T13:13:30Z
dc.date.available	2021-02-11T13:13:30Z
dc.date.issued	2013	*
dc.date.submitted	2013-09-20 18:00:24	*
dc.identifier	15505	*
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/47161
dc.description.abstract	Clustering or cluster analysis [5] is a method in unsupervised learning and one of the most used techniques in statistical data analysis. Clustering has a wide range of applications in many areas like pattern recognition, medical diagnostics, datamining, biology, market research and image analysis among others. A cluster is a set of data points that in some sense are similar to each other, and clustering is a process of partitioning a data set into disjoint clusters. In distance clustering, the similarity among data points is obtained by means of a distance function.	*
dc.language	English	*
dc.relation.ispartofseries	Mathematical Sciences	*
dc.subject	QA1-939	*
dc.subject.classification	bic Book Industry Communication::P Mathematics & science	en_US
dc.subject.other	Mathematical	*
dc.title	Exact algorithms for size constrained clustering	*
dc.type	book
oapen.relation.isPublishedBy	cb2a1db5-5754-4ab6-bb64-d635458e30c5	*
oapen.relation.isbn	9788867050659	*

Files in this item

Files	Size	Format	View
There are no files associated with this item.

Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-sa/4.0/