TY  - JOUR
T1  - Genetic Algorithm Based Dimensionality Reduction for Improving Performance of K-Means Clustering: A Case Study for Categorization of Medical Dataset
AU - Karegowda, Asha Gowda AU - T. Shama, Vidya AU - Jayaram, M.A. AU - Manjunath, A.S. 
JO  - International Journal of Soft Computing
VL  - 7
IS  - 5
SP  - 249
EP  - 255
PY  - 2012
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2012.249.255
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2012.249.255
KW  - k-means clustering
KW  -genetic algorithm
KW  -dimensionality reduction
KW  -wrapper approach
KW  -cluster center initialization
KW  -entropy based fuzzy clustering
KW  -medical dataset
AB  - Medical data mining is the process of extracting hidden patterns 
  from medical data. Among the various clustering algorithms, k-means is the one 
  of most widely used clustering technique. The performance of k-means clustering 
  depends on the initial cluster centers and might converge to local optimum. 
  k-means does not guarantee unique clustering because it generates different 
  results with randomly chosen initial clusters for different runs of k-means. 
  In addition the performance of any data mining depends on feature subset selection. 
  This study attempts to improve performance of k-means clustering using two stages. 
  As part of first stage, this study investigates the use of wrapper approach 
  for feature selection for clustering where Genetic Algorithm (GA) is used as 
  a random search technique for subset generation, wrapped with k-means clustering. 
  In second stage, GA and Entropy based Fuzzy Clustering (EFC) are used to find 
  the initial centroid for k-means clustering. Experiments have been conducted 
  using standard medical dataset namely Pima Indians Diabetes Dataset (PIDD) and 
  Heart statlog. Results show markable reduction of 8.42 and 18.89% in the classification 
  error of k-means clustering for PIDD and Heart statlog dataset using features 
  identified by proposed wrapper approach and initial centroids identified by 
  GA when compared to k-means performance with all the features and centroids 
  initialized by random method for PIDD and Heart statlog dataset.
ER  -