Map Reduce Based FiDoop
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Date
2017-08-18T09:39:36Z
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Abstract
Existing parallel mining algorithms for frequent itemsets lack a mechanism that enables
automatic parallelization, load balancing, data distribution, and fault tolerance on large
clusters. As a solution to this problem, we design a parallel frequent itemsets mining
algorithm called FiDoop using the MapReduce programming model. To achieve compressed
storage and avoid building conditional pattern bases, FiDoop incorporates the frequent items
ultrametric tree, rather than conventional FP trees. In FiDoop, three MapReduce jobs are
implemented to complete the mining task. In the crucial third MapReduce job, the mappers
independently decompose itemsets, the reducers perform combination operations by
constructing small ultrametric trees, and the actual mining of these trees separately.
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1NH12IS078, Map Reduce Based FiDoop, Jyothi Jenifer, ISE Projects 2017