A fast seeding technique for k-means algorithm.

dc.contributor.advisor	Hamerly, Gregory James, 1977-
dc.creator	Karbasi, Seyedeh Paniz, 1986-
dc.date.accessioned	2015-03-18T16:30:48Z
dc.date.available	2015-03-18T16:30:48Z
dc.date.created	2014-12
dc.date.issued	2014-11-06
dc.date.submitted	December 2014
dc.date.updated	2015-03-18T16:30:48Z
dc.description.abstract	The k-means algorithm is one of the most popular clustering techniques because of its speed and simplicity. This algorithm is very simple and easy to understand and implement. The first step of this algorithm is choosing k initial cluster centers. The way that this set of initial cluster centers are chosen, have a great effect on speed and quality of k-means. One of the most popular seeding techniques is k-means++ initialization, but this method needs k passes over the dataset. The goal of this thesis is to propose a new seeding technique which chooses the initial centers much faster than k-means++.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2104/9248
dc.language.iso	en
dc.rights.accessrights	Worldwide access.	en_US
dc.subject	k-means. Seeding. Clustering.
dc.title	A fast seeding technique for k-means algorithm.
dc.type	Thesis
dc.type.material	text
thesis.degree.department	Baylor University. Dept. of Computer Science.
thesis.degree.grantor	Baylor University
thesis.degree.level	Masters
thesis.degree.name	M.S.