Sliding window association rule mining | |
| Author | Onanong Nopkhun |
| Call Number | AIT Thesis no.CS-03-27 |
| Subject(s) | Barter Exchange Telemarketing Data mining |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. CS-03-27 |
| Abstract | In rapidly thriving barter exchange business, human brokers as business mediators take care of all their members in various ways such as what they will purchase, what products or services will be provided, and purchase recommendation. For recommendation, brokers need to observe all their members’ purchase behavior. How effective the recommendation is depends on an individual broker’s experience. In order to help new brokers generate useful recommendation in a short time, a recommendation engine, a semi-automated broker, is proposed in this thesis. The objective is to observe and predict purchases based on sparse real business transactions. Among several data mining techniques, a novel approach, Sliding Window Association Rule Mining (SWARM) algorithm, is proposed to discover the association rules from temporal transactional data. The correlations of products purchased within a given time window are considered as the rules which are used for recommendations at appropriate thresholds. In the experiment, three parameters: minconf, minsup and winsize, were used to find out the affect of these parameters on the association rules. The experimental results of testing with real data show reasonable and accurate association rules. The results were evaluated via an evaluation program and a human expert. First, the evaluation program shows that small time window is found to be slightly more helpful than large time window. Second, the human expert who classified how useful or interesting these rules are shows that there are still some rules relatively useless to generate recommendations. As one of the confidence measures, lift is appropriated for ranking top-N rules and also improving the generated rules to become more efficient for recommendations. |
| Year | 2003 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. CS-03-27 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Haddawy, Peter; |
| Examination Committee(s) | Phan Minh Dung;Vatcharaporn Esichaikul; |
| Scholarship Donor(s) | Royal Thai Government; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2003 |