TY  - JOUR
UR  - http://www.plosone.org
N2  - 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 minimize
the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm,
which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is
based on the recently established relationship between principal component analysis and the k-means clustering. We
provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and
six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is
empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the
clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the
quality is good (ARIHA.0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA.0.9).
In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to
microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm
can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the
members is used. This has been demonstrated in this work on six non-biological data
TI  - Reducing the Time Requirement of k-Means Algorithm
JF  - PLOS ONE
A1  - Osamor, V. C.
A1  - Adebiyi, E. F.
A1  - Oyelade, O. J.
A1  - Doumbia, Seydou
AV  - public
VL  - 7
Y1  - 2012/12//
IS  - 12
ID  - eprints12537
ER  -