Multilevel Interesting Association Rule Mining Using Soft Computing Techniques

Data warehouse contains large amounts of data from a various sources that may contain some noise while using for decision making. Data mining is extraction of knowledge from large data which may contains some amount of missing data along with inaccurate data and outliers. One of the best ways to detect data errors is by properly utilizing association rules that indicates relationships among attributes. Association rule mining algorithms detects patterns which occur in large dataset. Mining association rules at multiple level of concept hierarchy lead to the detection of more specific and actual knowledge from the dataset. The present paper uses various soft computing approaches for mining multilevel interesting association rules. In real-world problems, transaction data contains quantitative values. The fuzzy logic is useful for finding interesting association rules in quantitative transactions. To generate optimized multilevel association rule, optimization techniques such as genetic algorithm, ant colony optimization and particle swarm optimization are used. In this paper, soft computing techniques are reviewed based on approach used, findings and open issues in order to find optimized multilevel interesting association rules.