Saturday 23rd of September 2017
 

New Approach to optimize the time of Association Rules Extraction


Thabet Slimani

The knowledge discovery algorithms have become ineffective at the abundance of data and the need for fast algorithms or optimizing methods is required. To address this limitation, the objective of this work is to adapt a new method for optimizing the time of association rules extractions from large databases. Indeed, given a relational database (one relation) represented as a set of tuples, also called set of attributes, we transform the original database as a binary table (Bitmap table) containing binary numbers. Then, we use this Bitmap table to construct a data structure called Peano Tree stored as a binary file on which we apply a new algorithm called BF-ARM (extension of the well known Apriori algorithm). Since the database is loaded into a binary file, our proposed algorithm will traverse this file, and the processes of association rules extractions will be based on the file stored on disk. The BF-ARM algorithm is implemented and compared with Apriori, Apriori+ and RS-Rules+ algorithms. The evaluation process is based on three benchmarks (Mushroom, Car Evaluation and Adult). Our preliminary experimental results showed that our algorithm produces association rules with a minimum time compared to other algorithms.

Keywords: Data Mining, Association Rules, Large Databases, Frequent Itemsets, Peano Trees (Ptree)

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ABOUT THE AUTHOR

Thabet Slimani
graduated at the University of Tunis (Tunisia Republic) and defended PhD. thesis with title "New approaches for semantic Association Extraction and Analysis". He has been working as an assistant Professor at the Department of Computer Science, Taif University. He is a member of Larodec Lab (Tunis University). His interests include semantic Web, data mining and web service. He is author of more than 20 scientific publications.


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