Asha Gowda  Karegowda, Vidya  T. Shama, M.A.  Jayaram, A.S.  Manjunath, 
    Genetic Algorithm Based Dimensionality Reduction for Improving Performance of K-Means Clustering: A Case Study for Categorization of Medical Dataset,
    International Journal of Soft Computing,
    Volume 7,Issue 5,
    2012,
    Pages 249-255,
    ISSN 1816-9503,
    ijscomp.2012.249.255,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2012.249.255)
    Abstract: 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.
    Keywords: k-means clustering;genetic algorithm;dimensionality reduction;wrapper approach;cluster center initialization;entropy based fuzzy clustering;medical dataset