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A novel Bit Vector Product algorithm for mining frequent itemsets from large datasets using MapReduce framework
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DOI：10.1007/s10586-017-1249-x.png)
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Frequent itemset mining (FIM) is an interesting sub-area of research in the field of Data Mining. With the increase in the size of datasets, conventional FIM algorithms are not suitable and efforts are made to migrate to the Big Data Frameworks for designing algorithms using MapReduce like computing paradigms. We too interested in designing MapReduce based algorithm. Initially, our Parallel Compression algorithm makes data simpler to handle. A novel bit vector data structure is proposed to maintain compressed transactions and it is formed by scanning the dataset only once. Our Bit Vector Product algorithm follows the MapReduce approach and effectively searches for frequent itemsets from a given list of transactions. The experimental results are present to prove the efficacy of our approach over some of the recent works.
Keyword:
Big Data
Bit Vector
Compression
Data Mining
Frequent itemset mining
MapReduce framework
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