Hybrid Intelligent Method for Association Rules Mining Using Multiple Strategies

Hybrid Intelligent Method for Association Rules Mining Using Multiple Strategies

Y. Djenouri, H. Drias, Z. Habbas
Copyright: © 2014 |Pages: 19
DOI: 10.4018/ijamc.2014010103
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Association rules mining has attracted a lot of attention in the data mining community. It aims to extract the interesting rules from any given transactional database. This paper deals with association rules mining algorithms for very large databases and especially for those existing on the web. The numerous polynomial exact algorithms already proposed in literature processed the data sets of a medium-size in an efficient way. However, they are not capable of handling the huge amount of data in the web context where the response time must be very short. Moreover, the bio-inspired methods have proved to be paramount for the association rules mining problem. In this work, a new association rules mining algorithm based on an improved version of Bees Swarm Optimization and Tabu Search algorithms is proposed. BSO is chosen for its remarkable diversification process while tabu search for its efficient intensification strategy. To make the idea simpler, BSO will browse the search space in such a way to cover most of its regions and the local exploration of each bee is computed by tabu search. Also, the neighborhood search and three strategies for calculating search area are developed. The suggested strategies called (modulo, next, syntactic) are implemented and demonstrated using various data sets of different sizes. Experimental results reveal that the authors' approach in terms of the fitness criterion and the CPU time improves the ones that already exist.
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Many algorithms for generating association rules have been proposed in literature. Some well known exacts algorithms are AIS Agrawal and Imielinski (1993), Apriori, Agrawal, R. and Ramakrishan (2004), Eclat Zaki (2000) and FP-Growth Han, J., Pei, J.(2004). AIS is very space consuming and requires too many passes over the whole database. Apriori is the best known algorithm for association rules mining. It is based on breadth first search strategy to count the supports of itemsets and uses a candidate generation function to exploit the downward closure property of support. FP-growth uses a FP-tree structure to compress the database and a divide-and-conquer approach, to decompose the mining tasks and the database as well. In Zaki,M J. (1999) the authors present an interesting survey about different exact and polynomial algorithms. However, because of the fast web development and growth of databases, they have become very quickly inefficient. Indeed, even if these polynomial algorithms can still calculate the association rule in a very short time, they remain limited face to our goal which is to extract all the rules from large databases in real-time.

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