Multi Objective Fuzzy Association Rule Mining With ABC Algorithm: A Survey

Computer Engineering

Data Mining is the process of obtaining high level knowledge by automatically discovering information from data in the form of rules and patterns. Data mining is most commonly used in attempts to induce association rules from transaction data. Association rule mining is a well-established method of data mining that identifies significant correlations between items in transactional data. An association rule is an expression XàY, where X and Y are a set of items. It means in the set of transactions. Fuzzy Association rule mining is an essential topic in Information retrieval mining field and produces all important Fuzzy association rules between attributes in the dataset because large data set records considered as transactions. In ABC algorithm, the position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution. In this paper ABC seems particularly suitable for multi-objective optimization mainly because of solution quality and the high speed of convergence that the algorithm presents for single-objective optimization. Proposed work examines a new Multi-Objective Fuzzy Rule Mining with ABC algorithm.

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