Sequential Pattern and Association Rule Mining with Big Sales data for Online Merchants

The goal of this research project is to answer the following two highly-coupled questions concerning essentially all online merchants: (1) which items in my current inventory are highly relevant and should be sold or recommended together? and (2) what new merchandise should be put into my current online store? The key to answering the above questions relies on efficient data mining techniques that discover interesting relations between merchandise in large-scale online sales transactions. The research problems of this project come directly from the practical demand of the partner organization, Terapeak. This project follows the company’s strategic plan in performing high-quality and more efficient market analysis with big data. New technologies to facilitate online shopping and attract online buyers are indispensable for Terapeak to remain competitive in the e-commerce market.

Faculty Supervisor:

Dr. Kui Wu

Student:

Cheng Chen & Jie Chen

Partner:

Terapeak

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Victoria