A Novel Big Data Approach to Classify Bank Customers - Solution by Combining PIG, R and Hadoop

A Novel Big Data Approach to Classify Bank Customers - Solution by Combining PIG, R and Hadoop

Автор: Lija Mohan, Sudheep Elayidom M.

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 9 Vol. 8, 2016 года.

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Large amount of data that is characterized by its volume, velocity, veracity, value and variety is termed Big Data. Extracting hidden patterns, customer preferences, market trends, unknown correlations, or any other useful business information from large collection of structured or unstructured data set is called Big Data analysis. This article explores the scope of analyzing bank transaction data to categorize customers which could help the bank in efficient marketing, improved customer service, better operational efficiency, increased profit and many other hidden benefits. Instead of relying on a single technology to process large scale data, we make use of a combination of strategies like Hadoop, PIG, R etc for efficient analysis. RHadoop is an upcoming research trend for Big Data analysis, as R is a very efficient and easy to code, data analysis and visualization tool compared to traditional MapReduce program. K-Means is chosen as the clustering algorithm for classification.

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BigData Analysis, Bank customer classification, Hadoop, PIG, R

Короткий адрес: https://sciup.org/15012552

IDR: 15012552

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