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Abstract
This study presents a fast and scalable multi-objective association rule mining technique using genetic algorithm from large database. The objective functions such as confidence factor, comprehensibility and interestingness can be thought of as different objectives of our association rule- mining problem and is treated as the basic input to the genetic algorithm. The outcomes of our algorithm are the set of non-dominated solutions. However, in data mining the quantity of data is growing rapidly both in size and dimensions. Furthermore, the multi-objective genetic algorithm (MOGA) tends to be slow in comparison with most classical rule mining methods. Hence, to overcome these difficulties we propose a fast and scalability technique using the inherent parallel processing nature of genetic algorithm and a homogeneous dedicated network of workstations (NOWs). Our algorithm exploit both data and control parallelism by distributing the data being mined and the population of individuals across all available processors. The experimental result shows that the algorithm has been found suitable for large database with an encouraging speed up.
Highlights
Association rule mining is an important problem in the rapidly growing field called data mining and knowledge discovery in databases (KDD)[1]
multi-objective genetic algorithm (MOGA) itself tends to be slow and as the data size is growing and the computation of fitness is very expensive, so we expect parallelism is the technique to overcome the sequential bottleneck of MOGA based association rule mining method and provide scalability to massive data sets and improving response time
Sample size and the number of rules generated by our parallel multi-objective genetic algorithm (PMOGA) are put in the Table 1
Summary
Association rule mining is an important problem in the rapidly growing field called data mining and knowledge discovery in databases (KDD)[1]. The task of association rule mining is to mine a set of highly correlated attributes/features shared among a large number of records in a given database. The objective functions like confidence factor[2]; comprehensibility[3] and interestingness[4] can be thought of as different criterion of association rule mining problem.
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