Date Mining with Ontologies: Implementations, Findings and by Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel

By Héctor Oscar Nigro, Sandra Elizabeth Gonzalez Cisaro, Daniel Hugo Xodo

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With the increase in available, cheap data storage, companies are keeping terabytes of information about their customers. Today, it is not outrageous to talk about one-to-one marketing. However, marketers face two problems. They may have information about previous customers, but how could they get personal information about potential customers? Secondly, if information about individuals is truly not accessible, how could they classify such individuals into small categories and then market effectively to these small categories?

Ank _ mn , D >  Raising, to Enhance Rule Mining in Web Marketing with the Use of an Ontology R3 (T) = meaning that one person of male gender in the age group (20-24) is interested in actress, singer, and actor. One issue arising at this point is whether we accept the generalization that has occurred. This was described in (Geller et al. 2005) and will also be discussed further below. After having raised the data item , any traditional association rule mining algorithm may be applied to the result of Raising, instead of applying it to the original data item .

Mannila, Toivonen, and Verkamo (1994) worked on improving algorithms for finding associations rules, by eliminating unnecessary candidate rules. Berzal, Blanco, Sanchez, and Vila (2001) have worked on a problem that is in some sense the diametrical opposite of the problem in this research. They are trying to eliminate misleading rules that are the result of too high support values. The problem of generating association rules when the available support is too low to derive practically useful rules is being addressed here.

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