Granule-based knowledge representation for intra and inter transaction association mining

Yang, Wanzhong (2009) Granule-based knowledge representation for intra and inter transaction association mining. PhD thesis, Queensland University of Technology.

Abstract

Abstract With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis. Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules. To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.

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ID Code: 30398
Item Type: QUT Thesis (PhD)
Supervisor: Li, Yuefeng, Xu, Yue, & Lau, Raymond
Additional Information: See related dataset: http://services.ands.org.au/home/orca/rda/
Keywords: knowledge discovery, knowledge representation, intratransaction association rule, intertransaction association rule, granule mining, multi-tier, association mapping
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Institution: Queensland University of Technology
Deposited On: 11 Feb 2010 05:10
Last Modified: 28 Oct 2011 19:55

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