Personalization on the Net using Web mining: introduction
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Post-mining: maintenance of association rules by wieghting
Information Systems
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
Information and Software Technology
A new method for ranking discovered rules from data mining by DEA
Expert Systems with Applications: An International Journal
A new method for ranking changes in customer's behavioral patterns in department stores
Proceedings of the 11th International Conference on Electronic Commerce
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
DSVIS'06 Proceedings of the 13th international conference on Interactive systems: Design, specification, and verification
UI-HCII'07 Proceedings of the 2nd international conference on Usability and internationalization
Locality sensitive hashing for sampling-based algorithms in association rule mining
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Association Rules Evaluation by a Hybrid Multiple Criteria Decision Method
International Journal of Knowledge and Systems Science
Ranking and selection of unsupervised learning marketing segmentation
Knowledge-Based Systems
Hi-index | 12.06 |
Data mining techniques, extracting patterns from large databases are the processes that focus on the automatic exploration and analysis of large quantities of raw data in order to discover meaningful patterns and rules. In the process of applying the methods, most of the managers who are engaging the business encounter a multitude of rules resulted from the data mining technique. In view of multi-faceted characteristics of such rules, in general, the rules are featured by multiple conflicting criteria that are directly related with the business values, such as, e.g. expected monetary value or incremental monetary value. In the paper, we present a method for rule prioritization, taking into account the business values which are comprised of objective metric or managers' subjective judgments. The proposed methodology is an attempt to make synergy with decision analysis techniques for solving problems in the domain of data mining. We believe that this approach would be particularly useful for the business managers who are suffering from rule quality or quantity problems, conflicts between extracted rules, and difficulties of building a consensus in case several managers are involved for the rule selection.