@article{MAKHILLIJSC20149221189,
    title = {OLS-Association Rule for Optimal Learning Sequence Using K-means in Educational Data Mining},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {2},
    pages = {103-108},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.103.108},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.103.108},
    author = {Murugananthan and},
    keywords = {Educational data mining,K-means,learning,sequence,optimal},
    abstract = {Education data mining is one of the new emerging research areas in intra data 
  mining domain. The main objective of applying data mining to educational data 
  is to analyse educational data contents, models to summarize/analyse the learner’s 
  discussions, etc. Education data mining concentrates on the computing process 
  models which focus on education context. Researchers proposed a new approach 
  in deriving new association rules for optimal learning sequence of students 
  and tutors using K-means Clustering algorithm; here data’s are visualized 
  and processed. The methodology increases the performance with the fast support 
  calculation and other significant techniques are introduced to improve the efficiency 
  of the association rule based mining process using K-means. The new approach 
  is compared with Apriori algorithm and the comparison results presented here 
  shows the algorithm is optimal than the traditional Apriori algorithm.}
    }