Reducing the Time Requirement of k-means
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 d-dimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance...
Published at PloS One
Published in 2012
Osamor V.C., Adebiyi E., Oyelade J. O., Doumbia S.
Osamor Victor Chukwudi » I am a Professor of Bioinformatics in the Department of Computer & Information Sciences, Covenant University, Nigeria. I hold a Ph.D degree in Computer Science, a Marie Curie Fellow/ERCIM fellow and a Visiting Scholar to the Institute of Informatics, University of Warsaw, Poland. My publications includes the authorship of three books and several articles in reputable ISI indexed journals... view full profile
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