@article{MAKHILLIJSC20149321194,
    title = {Behaviour Analysis Model for Social Networks using Genetic Weighted Fuzzy C-Means Clustering and Neuro-Fuzzy Classifier},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {3},
    pages = {138-142},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.138.142},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.138.142},
    author = {P. Indira,D.K.,A. and},
    keywords = {Clustering,Genetic algorithms,global optimization,Weighted Fuzzy C-Means algorithm,Weighted Fuzzy C-Means algorithm},
    abstract = {Genetic algorithms are helpful to make effective decisions using suitable 
  fitness functions. They can be used to perform both clustering and classification. 
  However, Clustering algorithms enhanced only with genetic operators are not 
  sufficient for making decision in many critical applications. In this study, 
  researchers propose a new user behaviour analysis model by combining Genetic 
  algorithm with Weighted Fuzzy C-Means Clustering Algorithm (GNWFCMA) for effective 
  clustering. The proposed clustering algorithm is used to improve the classification 
  accuracy by providing initial groups. In addition, researchers use a five factor 
  analysis also for effective clustering. Finally, researchers use a neuro-fuzzy 
  classifier for classifying the data. The experimental results obtained from 
  this study shows that the clustering results when combined with classification 
  algorithm provides better classification accuracy when tested with Weblog dataset.}
    }