Data mining is the process of analysing data from many different perspectives or dimensions, categorize it and finally summarize it into useful information. This information can be used to increase profits, cut costs, or both. Data mining software is used for analysing data. It allows users to analyse data from many different perspectives, categorize it, and summarize the relationships discovered. Specially, data mining is the way of extracting valuable correlations or patterns among many number of fields in large relational databases. Pattern mining has become an important task in data mining. Mining frequent and infrequent itemsets from a dataset is the most important field of data mining. Mining frequent itemset is very expensive when minimum support threshold is low, and when a minimum support threshold is high mining in frequent itemsets is highly expensive. The proposed system uses multiple level minimum supports to constrain infrequent itemsets by giving different minimum supports to itemsets with different length in order to mine a number of infrequent itemsets in an appropriate degree. In this paper, we are implementing the concept of infrequent weighted itemset mining based on Hadoop-MapReduce model, which can handle massive datasets in mining in frequent itemsets, in that we proposed two novel algorithms based on IWI Miner, IWI Miner to drive the IWI mining process. This paper emphasis on the issue of discovering those itemsets which occurs rarely in large dataset called infrequent weighted itemset (IWI) mining problem.

Data Mining, frequent Itemset, Infrequent Itemset, Weighted Itemset, Hadoop , MapReduce

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