26-08-2016, 11:52 AM
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ABSTRACT:
A common problem for many companies, like retail stores, it is to find sets of products that are sold together. The only source of information available is the history of sales transactional data. Common techniques of market basket analysis fail when processing huge amounts of scattered data, finding meaningless relationships. We developed a novel approach for market basket analysis based on graph mining techniques, able to process millions of scattered transactions. We demonstrate the effectiveness of our approach in a wholesale supermarket chain and a retail supermarket chain, processing around 238,000,000 and 128,000,000 transactions respectively compared to classical approach
DOMAINS WHERE THE PROJECT CAN BE IMPLEMENTED:
Our project can be implemented for the following purposes,
Huge super markets
Retail stores
Outlets
IMPLEMENTATION:
This project will be implemented using Java software platform.
PRINCIPAL IDEA:
This work is focused on generating frequent item sets of products based on transactional data generated by a retail chain. The main idea is to obtain sets of meaningful products so we can generate customer profiles, product layout and recommendations from related products.
PLAN OF ACTION :
Phases Time allowance Target delivery date
Analysis 8W Nov 25th
Design 12W Feb 20th
Coding
And Implementation 5W Mar 25th