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A Fuzzy Based Model for Mining Conditional Hybrid Dimensional Association Rules

Neelu Khare, Neeru Adlakha, K. R. Pardasani


Mining association rules in transactional or relational databases is an important task in data mining. Fuzzy predicates have been incorporated into association rule mining to extend types of data relationships that can be represented, for interpretation of rules in linguistic terms and to avoid fix boundaries in partitioning data attributes. In this paper, the mining of single dimensional association rule and non-repetitive predicate multi-dimensional association rule are combined over the transactions of multidimensional transaction database. The algorithm mines conditional hybrid dimension association rules which satisfy the definite condition on the basis of multi-dimensional transaction database. In this algorithm each predicate should be partitioned at the fuzzy set level, the support count of itemsets is calculated by performing fuzzy AND operation on items that constitute the itemsets. Apriori property is used in algorithm to prune the item sets. The implementation of algorithm is illustrated with the help of a simple example.


Fuzzy Set Level, Hybrid Dimensional Association Rule, Multi-Dimensional Transaction Database, Conditional Restrict, Predicate.

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