An Efficient Mining Approach of Frequent Data Item Sets on Large Uncertain Databases

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-20 Number-1
Year of Publication : 2015
Authors : Isse Hassan Sheikh Nur
  10.14445/22312803/IJCTT-V20P107

MLA

Isse Hassan Sheikh Nur "An Efficient Mining Approach of Frequent Data Item Sets on Large Uncertain Databases". International Journal of Computer Trends and Technology (IJCTT) V20(1):37-40, Feb 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Mining frequent items from large uncertain database is a crucial issue, according to the accuracy, performance and computational cost, where we need the frequent itemset is ascertained efficiently and accurately with low computational cost and high performance in detecting probabilistic frequent item (PFI), so all of these factors are the required or recommended in a large uncertain database to extract the frequent items efficiently and accurately. In uncertain database the support of an item occurs randomly instead of fixed variable. We will use a model based algorithm and dynamic algorithm for mining and generating candidate itemsets for frequent itemsets in large uncertain data. Our goal is a better performance based on our dataset.

References
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[4] M. W.David, “Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation,” Journal of Machine Learning Technologies, vol. 2, pp. 37–63, 2011.

Keywords
Frequent itemsets, Probabilistic Frequent Item