@article{MAKHILLAJIT20141345822,
    title = {Data Clustering using GA with Non-Negative Matrix Factorization},
    journal = {Asian Journal of Information Technology},
    volume = {13},
    number = {4},
    pages = {222-234},
    year = {2014},
    issn = {1682-3915},
    doi = {ajit.2014.222.234},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2014.222.234},
    author = {K.S.,K.V. and},
    keywords = {Data mining,Genetic algorithm,PSO,K-Means algorithm,clustering,matrix factorization},
    abstract = {In this study, a new approach has applied to define the clustering 
  using factorizing the original data set matrix into two lower dimension matrices 
  namely, two dimensional features data set and a transformation matrix with the 
  help of non negative matrix factorization. This two dimensional feature data 
  set is having the more separation in available different categories and also 
  provide approximated visual information about possible clusters available in 
  data set along with correlation available among them. Two dimension feature 
  sets are a used to obtain the final clusters using optimizing the minimum quantizing 
  error with help of Genetic algorithm. Comparisons are made with other well established 
  algorithms like particle swarm optimization. Benefits of features matrix is 
  also shown in compare to raw data set in terms of obtained cluster performance. 
  K-means algorithm is also applied independently before and after matrix factorization 
  and comparisons are made with other obtained results. Cluster performance indexes 
  are defined in terms of F-measure and purity.}
    }