Abstract
Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata
Highlights
Data mining is the process of exploring great amounts of data from transactional databases to obtain interesting information [1]-[2]
We addressed the above issue and proposed a novel algorithm that uses continuous action-set learning automata (CALA), named CALA-AFTM, to find position and the number of trapezoidal membership functions (TMFs) in fuzzy association rule mining at the same time
We evaluated the results obtained using the CALA-AFTM, VSLA-AFTM, and fuzzy web mining algorithm (FWMA) algorithms on the CTI and NASA datasets
Summary
By increasing the volume of data in the databases, effective techniques are required to manage the data in these databases. Some research papers have proposed algorithms for extracting fuzzy association rules [7], [27]-[35]. These methods only consider triangular membership functions that are not suitable for. We addressed the above issue and proposed a novel algorithm that uses continuous action-set learning automata (CALA), named CALA-AFTM, to find position and the number of TMFs in fuzzy association rule mining at the same time. In this paper, finding positions and number of TMFs for each membership function has been regarded as parameters of the search space To find these optimal parameters, a novel representation was suggested to build a CALA team. The proposed approach dynamically determines the position and number of TMFs in mining fuzzy association rule.
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