Mining Association Rules in Data Warehouses

Abstract

Data mining applications have enormously altered the strategic decision-making processes of organizations. The application of association rules algorithms is one of the well-known data mining techniques that have been developed to cope with multidimensional databases. However, most of these algorithms focus on multidimensional data models for transactional data. As data warehouses can be presented using a multidimensional model, in this paper we provide another perspective to mine association rules in data warehouses by focusing on a measurement of summarized data. We propose four algorithms-VAvg, HAvg, WMAvg, and ModusFilter-to provide efficient data initialization for mining association rules in data warehouses by concentrating on the measurement of aggregate data. Then we apply those algorithms both on a non-repeatable predicate, which is known as mining normal association rules, using GenNLI, and a repeatable predicate using ComDims and GenHLI, which is known as mining hybrid association rules.

Data warehousing and mining technologies for adaptability in turbulent resources business environments

“…Taniar et al (2008) discussed the rule-mining procedures and exceptions in their usages in different categories. Tjioe and Taniar (2005) demonstrated associative mining rules in the context of data warehouses. Hair et al (1984) described multivariate data analysis along with an application of time-varying industrial data.…”

Mining Triadic Association Rules

“…Moreover, they believe that classical association rules are limited only to the COUNT aggregate and consequently, can only express changes in the body of the rule. [22] present a method of mining association rules in data warehouses. Based on the multi-dimensional structuration of data, it is a method capable of extracting associations from multiple dimensions at multiple levels of abstraction according to the COUNT measure.…”

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).…”