Granule mining oriented data warehousing model for representations of multidimensional association rules

Abstract

To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted.

Toward intelligent data warehouse mining: An ontology-integrated approach for multi-dimensional association mining

“…In 2000, Psaila and Lanzi studied multi-level association mining from a primitive data warehouse and proposed a mining algorithm. Since then, substantial works have been devoted to discovering multidimensional association rules from data warehouses (Ng et al, 2002;Chung & Mangamuri, 2005;Tjioe & Taniar, 2005;Messaoud et al, 2006;Yang et al, 2008).…”

A knowledge-based model using ontologies for personalized web information gathering

“…Li and Zhong [42] represented semantic concepts by maximal patterns, sequential patterns, and closed sequential patterns, and extracted semantic concepts from Web documents. Association rule mining was also used by many systems for knowledge discovery from web documents, including [39,78,79]. Text clustering techniques were used by [21,35,76,85] to discover user interest for personalized Web information gathering.…”

Concept-Based, Personalized Web Information Gathering: A Survey

“…Association rules mining extracts meaningful content from Web documents and discovers their underlying knowledge. Existing models using association rules mining include Li and Zhong [26], Li et al [25], and Yang et al [68], who used the granule techniques to discover association rules; Xu and Li [67] and Shaw et al [48], who attempted to discover concise association rules; and Wu et al [66], who discovered positive and negative association rules. Some works, such as Dou et al [10], attempted to integrate multiple Web content mining techniques for concept extraction.…”