Abstract
Association rule mining is an important approach to data mining. It extracts useful and hidden information. There are two methodologies to explore the association rules. One method is generating frequent pattern generation through apriori like algorithms whereas another methodology is by using the soft computing techniques especially genetic algorithm. Two important aspect which is most of the time unaddressed, is incremental data and multi-objective. Very few research work on incremental and multi-objective association rule mining has been done. This paper comprises of a comprehensive study of incremental data mining and a distinct study of genetic algorithms. It is observed that soft-computing technique perform better for association rules. There is also a need for Incremental algorithms which work better in the state of addition, deletion and modification of data. It is also found that strong need of Multi-objective Incremental association rule mining algorithm.
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