FiDoop: An Interactive GUI to Identify Frequent Items Using Map Reduce

Raksha D, P Hari Prasad Reddy, Mukesh P U, Prof. Raghavendra Reddy


Due to an exponential increase of real-time data monitoring systems, the extraction of frequent itemset from the large database is a challenging task. Memory usage and excessive runtime for less amount of data, automatic parallelization are the limitations in existing algorithms of frequent itemsets. FiDoop based itemset algorithm is introduced by using MapReduce framework to overcome this problem. This system includes activities such as data uploading, preprocessing, threshold, find support and confidence, merge and result. We implement FiDoop on our in-house Hadoop cluster. To improve FiDoop’s performance a workload balance matric is used to measure load balancing across the cluster's computing node is developed. Initially, data is selected from the dataset and uploaded to the server, then the preprocessing stage removes columns which contain unwanted entries. The information is analyzed and partitioned to compute threshold value. Finally, frequent itemsets are merged to acquire frequent pattern. This proposed system is mainly developed for improving accuracy and is evaluated based on the performance measures.


FiDoop, MapReduce, Frequent itemset mining

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