Abstract—Knowledge discovery is a series of steps to extract useful information from data sets containing data in large volume. Nowadays, data sources contain large number of dimensions and data size is getting increased as a result. Data is archived in data warehouses now in an aggregate form. Data mining techniques to extract knowledge from datasets are now being applied in data warehouse. Interesting patterns are extracted in the form of association rules from data warehouses whereas interestingness measures are used to evaluate these patterns. The techniques available for evaluation of association rules were originally developed for transactional databases. In this research work, we enhance our previous methodology which extracts association rules in a data warehouse environment at multiple levels of abstraction. In this work we evaluate these association rules using advanced measures of interestingness particularly targeting the diversity measures. We have applied 9 measures of interestingness on association rules generated in the data warehouse and shown our results for diversity. Results further suggest that there is a strong correlation between some of these measures at cluster level. A future study can be conducted to deduce a linear model for prediction of diversity measures at lower levels in the hierarchy.
Index Terms—Association rule mining, interestingness, data warehouse, multi-level mining.
Muhammad Usman is with Pakistan Scientific and Technological Information Center, QAU Campus, Islamabad, Pakistan (e-mail: usmiusman@gmail.com).
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Cite: Muhammad Usman, "Measuring Diversity of Associations Rules Extracted from A Data Warehouse," International Journal of Knowledge Engineering vol. 4, no. 1, pp. 60-67, 2018.
