An Optimal Approach to derive Disjunctive Positive and Negative Rules from Association Rule Mining using Genetic Algorithm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2013, Vol 13, Issue 1
Abstract
Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. Association rule mining is one of the most important techniques of data mining that aims to induce associations among sets of items in transaction databases or other data repositories. There are various Algorithms developed and customized to derive the effective rules to improve the business. Amongst all, Apriori algorithms and FP Growth Algorithms play a vital role in finding out frequent item set and subsequently deriving rule sets based on business constraints. However there are few shortfalls in these conventional Algorithms. They are i) candidate items generation consumes lot of time in the case of large datasets ii) It supports majorly the conjunctive nature of association rules iii) The single minimum support factor not suffice to generate the effective rules iv) ‘support/confident’ alone not helping to validate the rules generated and v) Negative rules are not addressed effectively. Points from i) to iv) were addressed in the earlier works [10][13] . However identifying and deriving negative rules are still a challenge. The proposed work is considered to be the extended version of our earlier work [13]. It focuses how effectively negative rules can be derived with the help of logical rules sets which was not addressed in our earlier work. For this exercise the earlier work has been taken as the reference and the appropriate modifications and additions are updated into it where ever applicable. Hence by using this approach conjunctive & disjunctive; positive& negative rules can be generated effectively in an optimized manner
Authors and Affiliations
Kannika Nirai Vaani. M
Keywords
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