Mining rare association rules in a distributed environment using multiple minimum supports | |
| Author | Jutamas Tempaiboolkul |
| Call Number | AIT Thesis no.IM-13-05 |
| Subject(s) | Electronic data processing--Distributed processing Data mining Computer algorithms |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. IM-13-05 |
| Abstract | Distributed data mining of association rules is an area of data mining which intends to find association rules over items geographically across the network. Several researches have been performed in this field as applications have started to exploit distributed databases. Discovering rare association rules is a new area of distributed mining research. In this paper, an algorithm for discovering rare association rules in distributed environment is proposed. It utilized the idea of using statistic percentile to produce multiple minimum supports to miner are association rules. Finally, the proposed algorithm has been implemented and evaluated by comparing with the Optimized Distributed Association rule Mining (ODAM) algorithm and the Apriori with Multiple Support Generating by statistic Percentile threshold (Apri-oriMSG-P) algorithm. The result shows that the proposed algorithm can discover more rare association rules with an optimized communication cost. |
| Year | 2013 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. IM-13-05 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Guha, Sumanta; |
| Examination Committee(s) | Phan Minh Dung;Duboz, Raphael; |
| Scholarship Donor(s) | Thailand (HM King); |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2013 |