Reducing the time requirement of k-means Algorithm.

~ Bddf58dbb411cefd645e70c20debcc32.200x200

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in ddimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimizethe mean squared distance...

Published at PloS One Journal Vol. 7(12) (IF: 4.25).

Published in 2014

Download 177.73 kB

visit (http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0049946, http://www.ncbi.nlm.nih.gov/pubmed?term=oyelade)

Oyelade, O.J.

Oyelade Olanrewaju Jelili » Dr. Oyelade, Olanrewaju Jelili Received his Bachelor Degree in Computer Science with Mathematics (Combined Honour) and M.Sc. in Computer Science from Obafemi Awolowo University, Ile-Ife, Nigeria and Ph.D in Covenant University, Ota, Nigeria. He is a Faculty member in the Department of Computer and Information Sciences, Covenant University, Nigeria. His current research interests include... view full profile