A NOVEL APPROACH TO MINE FREQUENT PATTERNS FROM LARGE VOLUME OF DATASET USING MDL REDUCTION ALGORITHM

In this paper, MDL based reduction in frequent pattern is presented. The ideal outcome of any pattern mining process is to explore the data in new insights. And also, we need to eliminate the non-interesting patterns that describe noise. The major problem in frequent pattern mining is to identify the interesting patterns. Instead of performing association rule mining on all the frequent item sets, it is feasible to select a sub set of frequent item sets and perform the mining task. Selecting a small set of frequent item sets from large amount of interesting ones is a difficult task. In our approach, MDL based algorithm is used for reducing the number of frequent item sets to be used for association rule mining is presented. MDL based approach provides good reduction of frequent patterns on all types of data such as sequences and trees. Experimental results show that reductions up to three orders of magnitude is achieved when MLD algorithm is used.