MISSING VALUE IMPUTATION USING VOTING BASED OPTIMIZED ASSOCIATIVE RULE MINING ALGORITHM

In real time applications, imputation of missing value is a definite and tough problem confronted by ma- chine learning and data mining. As a result, there are many attempts to missing value imputation. To overcome this respective issue, the experimental study has been carried out using continuous and dis- crete from UCI repository to shows that the proposed work is well effective than the other existing sys- tems. Voting is made to find out the best candidate having the highest vote is finally chosen as the im- puted value and the proposed system increases the accuracy rate and greatly reduces the error rate. The proposed voting based optimized ARM algorithm outperforms with less RMSE values than Robust As- sociative Rule Mining Algorithm for 80% missing rate respectively.