Data Elimination based technique for mining frequent closed item set

Frequent pattern mining is top chart research field for young researchers. It has a huge array of real world applications. Although many algorithms, tools, techniques are available for performing the task of frequent pattern mining. Apriori and FP growth are very popular frequent pattern mining techniques. This paper presents an updated methodology for frequent closed item set mining. The proposed model is based on the concept of data reduction. Useless data is eliminated from the transaction data base. The experimental results have shown that the proposed updated method is outperforming the existing methods. We have performed an extensive result analysis. We compared proposed technique with previous technique on parameters like no of items generated, amount of time consumes, amount of space consumed etc.