Interestingness Classification of Association Rules for Master Data
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High quality of master data is crucial for almost every company and it has become increasingly difficult for domain experts to validate the quality and extract useful information out of master data sets. However, experts are rare and expensive for companies and cannot be aware of all dependencies in the master data sets. In this paper, we introduce a complete process which applies association rule mining in the area of master data to extract such association dependencies for quality assessment. It includes the application of the association rule mining algorithm to master data and the classification of interesting rules (from the perspective of domain experts) in order to reduce the result association rules set to be analyzed by domain experts. The model can learn the knowledge of the domain expert and reuse it to classify the rules. As a result, only a few interesting rules are identified from the perspective of domain experts which are then used for database quality assessment and anomaly detection.
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- 1.Hipp, J., Müller, M., Hohendorff, J., Naumann, F.: Rule-based measurement of data quality in nominal data. In: ICIQ, pp. 364–378 (2007)Google Scholar
- 3.The main important sap material master tables ( data & customizing ). http://sap4tech.net/sap-material-master-tables/ (accessed on February 20, 2017)
- 5.Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. ACM Sigmod Record 25(2), 1–12 (1996)Google Scholar
- 6.Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I., et al.: Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining 12(1), 307–328 (1996)Google Scholar
- 7.What is pal? – sap hana platform, https://help.sap.com/viewer/2cfbc5cf2bc14f028cfbe2a2bba60a50/2.0.00/en-US (accessed on February 20, 2017)
- 8.Zaki, M.J.: Generating non-redundant association rules. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 34–43. ACM (2000)Google Scholar
- 9.Strehl, A., Gupta, G.K., Ghosh, J.: Distance based clustering of association rules. In: Proceedings ANNIE, vol. 9(1999), pp. 759–764. Citeseer (1999)Google Scholar
- 10.Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)Google Scholar
- 11.Fawcett, T.: Roc graphs: Notes and practical considerations for researchers. Machine Learning 31(1), 1–38 (2004)Google Scholar