Applications of Data Mining to Electronic Commerce by Ron Kohavi, Foster Provost (auth.), Ron Kohavi, Foster

By Ron Kohavi, Foster Provost (auth.), Ron Kohavi, Foster Provost (eds.)

Applications of information Mining to digital Commerce brings jointly in a single position very important contributions and updated learn leads to this fast paced region.
Applications of information Mining to digital Commerceserves as a very good reference, supplying perception into probably the most difficult learn matters within the field.

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The proposed method provides a new approach to deriving association rules that segment users based on their transactional characteristics. However, it does not derive behavior of an individual user in a one-to-one fashion (Peppers and Rogers, 1993). Still another approach to the profiling problem was presented by Chan (1999) in the context of providing personalized Web search. In this approach the user profile consists of a Web Access Graph summarizing Web access patterns by the user, and a Page Interest Estimator characterizing interests of the user in various Web pages.

Future Generation Computer Systems, 13(2). 32 LAWRENCEET AL. Resnick, P. R. 1997. Recommender systems. Communications of the ACM, 40(3):56-58. Also see other articles in this special issue. Robbins, H. and Van Ryzin, J. 1975. Introduction to Statistics. Scientific Research Associates, Inc .. Salton, 1. 1989. Automatic Text Processing: The Transformation, Analysis and Retrieval of Information By Computer. Reading, MA: Addison-Wesley. Salton,1. J. 1983. Introduction to Modem Information Retrieval.

The same group ifand only ifcutc(R\} = cutc(R2}. Naturally, two different aggregated rules represent two disjoint groups of rules. As an example, figure 4 presents the results of grouping a set of rules based on the attribute hierarchy and several different cuts shown in figure 3. The grouping operator described above allows the user to group rules into sets of similar rules, where similarity is defined by the expert who selects a specific cut of the attribute hierarchy. Moreover, instead of examining and validating individual rules inside each group, the user can examine the group of these rules as a whole based on the aggregated rule (that is common for all the rules in the group) and decide whether to accept or reject all the rules in that group at once based on this aggregated rule.

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