TY  - JOUR
T1  - OLS-Association Rule for Optimal Learning Sequence Using K-means in Educational Data Mining
AU - Velayutham, Murugananthan AU - ShivaKumar, B.L. 
JO  - International Journal of Soft Computing
VL  - 9
IS  - 2
SP  - 103
EP  - 108
PY  - 2014
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2014.103.108
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2014.103.108
KW  - Educational data mining
KW  -K-means
KW  -learning
KW  -sequence
KW  -optimal
AB  - 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.
ER  -