TY  - JOUR
T1  - An Initialization Method for K-Means Algorithm Using Binary Search Technique
AU - Kumar, Yugal AU - Sahoo, G. 
JO  - International Journal of Soft Computing
VL  - 9
IS  - 3
SP  - 131
EP  - 137
PY  - 2014
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2014.131.137
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2014.131.137
KW  - Clustering
KW  -cluster centers
KW  -K-means
KW  -binary search
KW  -accuracy rate
AB  - K-Means algorithm is most popular partition based algorithm that is widely 
  used in data clustering. A lot of algorithms have been proposed for data clustering 
  using K-Means algorithm due to its simplicity, efficiency and ease convergence. 
  In spite this K-Means algorithm has some drawbacks like initial cluster centers. 
  In this study, a new method is proposed to address the initial cluster centers 
  problem in K-means based on binary search technique. The initial cluster centers 
  is obtained using Binary Search Method and the newly generated cluster centers 
  are used as initial cluster centers in K-means to gain optimal cluster centers 
  in dataset. The performance of the Proposed algorithm is tested on the two benchmark 
  dataset iris and wine that are downloaded from the UCI machine learning repository 
  and compare the proposed method with Random, Hartigan and Wang, Ward, Build, 
  Astrhan, Minkowaski ward and IWKM Method in which proposed method with K-means 
  provides 82.93 and 68.94 accuracy rate and intra cluster distance is 105.72 
  and 18059.81 with iris and wine datasets as well as proposed method with IWKM 
  provides 96.7 and 95.8 accuracy rate.
ER  -