Frequent Item Set using Transaction Reduction Technique in Data Mining - A Survey

Data mining has many useful applications in recent year and it can help to discover the interesting knowledge in a large data set. Data reduction becomes a challenging issue in the data mining. Data reductions easily make the availability of the required space. Here analysis of the simple Apriori and the apriori over reduction data set using the transaction reduction technique, existing reduction technique are not appropriate for data mining due to lack of consistency of maintaining the reduced data set. Three different approaches are proposed in which first is to sort the data set, the second one is grouping the data set and last is merging the data set. By performing this process it can define the time variation in processing time for large data set by using the reduction technique in this research an approach called DSMRTT-Data Sorted merging reduction table Techniques are used by sorting and merging technique to solve this problem and reduce the data set. Then reduce dataset is maintained in the future as input of Apriori algorithm. The apriori algorithm is used to find the frequent item set in the data set. As simple apriori generates extremely large number of redundant rules which makes the algorithm inefficient and it does not fit in main memory. Therefore the proposed work will take care of these issues and will try to solve it.