Effective Implementation of Apriori Algorithm to Develop a Suggestion System Based on Sales History Using Hadoop Environment

Abstract

Abstract

The need for comprehensive support system to analyze and predict the nature of the dynamic market based on the previous records is very vital in competent industries today. The data mining is a process of extracting implicit, previously unknown and potentially useful information from data. Mining is search for relationships and global patterns that exist in the large databases but that are hidden among vast amount of data. Since the data collected tends to be huge and complex, it is necessary to structure it and process it using evolving technologies like Hadoop. Apriori algorithm is an algorithm for frequent itemsets mining and association rule learning over transactional database. This algorithm helps in finding out the frequent itemsets and thus deriving the association rule between the itemsets. Apriori algorithm when used in Hadoop framework is proven to be highly effective, with regard to time complexity. A MapReduce framework is a major part of Hadoop and performs filtering and sorting and a summary operation. This paper aims at providing solution to the task of analyzing the sales and distribution function of an enterprise using the latest Hadoop technology.

Keywords: A priori algorithm, data mining, frequent itemsets, association rules, Hadoop MapReduce

Cite this Article

Aishwarya Rani MR, Shivanand RD. Effective Implementation of Apriori Algorithm to Develop a Suggestion System Based on Sales History Using Hadoop Environment. Journal of Advanced Database Management & Systems. 2016; 3(3): 8–16p.