Weighted Itemset Mining from Bigdata using Hadoop

Data items have been extracted using an empirical data mining technique called frequent itemset mining. In majority of theapplication contexts items are enriched with weights. Pushing an item weights into the itemset extraction process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient weighteditemset mining algorithms are available in literature, there isa lack of parallel and distributed solutions which are able to scale towards Big Weighted Data. This Proposed work presents an efficient frequent weighted itemset mining algorithm based on the MapReduce paradigm. It adopts the MapReduce architecture to partition thewhole mining tasks into smaller independent subtasks and uses Hadoop distributed file system to manage distributed data so that it allows the parallel and distributed solution.To demonstrate its actionability and scalability, the proposed algorithm will be tested on a Bigdataset collecting large volume of reviews ofitems. Weights indicate theratings given by users to the purchased items. The mined itemsets represent combinations of items that were frequently bought together with an overall rating above average. Keywords-MapReduce, Parallel Computing, hadoop, frequentitemset, Data mining, Distributed Computing, Apriori Algorithm.