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FI-DBSCAN: Frequent Itemset Ultrametric Trees with Density Based Spatial Clustering Of Applications with Noise Using Mapreduce in Big Data

V.Swathi Kiruthika, Dr.V.Thiagarasu

Data mining is gaining importance due to huge amount of data available. Retrieving information from the warehouse is not only tedious but also difficult in some cases. The existing algorithm does not provide fast computation and better result. Frequent itemset using density based spatial clustering is used in the proposed system so that support is counted by mapping the items from the candidate list into the buckets which is divided according to support known as Hash table structure. As the new itemset is encountered if item exist earlier then increase the bucket count else insert into new bucket. Thus in the end the bucket whose support count is less the minimum support is removed from the candidate set.