Notice of Retraction Data Mining Itemset of Big Data Using Pre-Processing Based on Mapreduce FrameWork with ETL Tools

Padmanathan Anantharaman, H.V. Ramakrishan

Abstract


Notice of Retraction

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After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.

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As data volumes continue to grow, they quickly consume the capacity of data warehouses and application databases. Is your IT organization forced into costly upgrades to expensive databases and data warehouse hardware appliances and enormous amount of data is getting explored through Internet of Things (IoT) as technologies are advancing and people uses these technologies in day to day activities, this data is termed as Big Data having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets but it has large communication cost which reduces execution efficiency. This proposed new pre-processed k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using k-means algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets from generated clusters using MapReduce programming model. Results shown that execution efficiency of ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as one of the pre-processing technique.


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DOI: https://doi.org/10.11591/APTIKOM.J.CSIT.103

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