Abstract
Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.
Highlights
In data-mining, discovering association rules in transaction databases is frequently examined
The purpose of this study is to offer to the field of data mining and in precise to multilevel quantitative fuzzy association rule mining
We have introduced a new genetic-based algorithm to mine multilevel association rules in big quantitative date sets that deals with quantitative attributes by accurate fuzzification the values -partitioning the values of the attribute
Summary
In data-mining, discovering association rules in transaction databases is frequently examined. Association rules are widely offered and are beneficial for planning and marketing. They can be managed to implicate supermarket officials of what products the customers have an inclination to purchase together. The classical algorithms for mining association rules are formed on binary attributes databases, which have two weaknesses. It cannot treat quantitative attributes; secondly, it handles each item with the same weight despite that strange item may have different importance. That is why numerous researchers have been serving on the generation of association rules for quantitative data [2] [3]
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