Abstract
In this paper, we develop and implement a framework for constraint-based association rule mining across subgroups in order to help a domain expert find useful patterns in a medical data set that includes temporal data. This work is motivated by the difficulties experienced in the medical domain to identify and track dyspepsia symptom clusters within and across time. Our framework, Apriori with Subgroup and Constraint (ASC), is built on top of the existing Apriori framework. We have identified four different types of phase-wise constraints for subgroups: constraint across subgroups, constraint on subgroup, constraint on pattern content and constraint on rule. ASC has been evaluated in a real-world medical scenario; analysis was conducted with the interaction of a domain expert. Although the framework is evaluated using a data set from the medical domain, it should be general enough to be applicable in other domains.
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References
Westbrook, J.I., Talley, N.J.: Empiric Clustering of Dyspepsia into Symptom Subgroups: a Population-Based Study. Scand. J. Gastroenterol. 37 (2002) 917–923
Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: Buneman, P., Jajodia, S. (eds.): Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. Washington, DC (1993) 207–216
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Fayyad, U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. Menlo Park, CA (1996) 307–328
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.), Proceedings of the Twentieth International Conference on Very Large Data Bases (VLDB’94). Santiago (1994) 487–499
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Proceedings of the Fifth International Conference on Extending Database Technology (EDBT’96). Avignon (1996) 3–17
Bayardo, R.J., Agrawal, R., Gunopulos, D.: Constraint-Based Rule Mining in Large, Dense Databases. In: Proceedings of the Fifteenth International Conference on Data Engineering. (1997) 188–197
Ng, R., Lakshmanan, L.V.S., Han, J., Pang, A.: Exploratory Mining and Pruning Optimizations of Constrained Associations Rules. In: Proceedings of ACM SIGMOD Conf. on Management of Data (SIGMOD’98). Seattle (1998) 13–24
Fu, Y., Han, J.: Meta-Rule-Guided Mining of Association Rules in Relational Databases. In: Proceedings of the First International Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD’95). Singapore (1995) 39–46
Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding Interesting Rules from Large Sets of Discovered Association Rules. In: Proceedings of the Third International Conference on Information and Knowledge Management (CIKM’94). Maryland (1994) 401–407
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2000)
Srikant, R., Vu, R., Agrawal, R.: Mining Association Rules With Item Constraints. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD’97). (1997) 67–73
Cohen, W.W.: Grammatically Biased Learning: Learning Logic Programs Using an Explicit Antecedent Description Language. Artif. Intell. 68 (1994) 303–366
Morik, K., Wrobel S., Kietz, J., Emde, W.: Knowledge Acquisition and Machine Learning. Academic Press (1994)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Yu, P.S., Chen, A.S.P. (eds.): Proceedings of the Eleventh International Conference on Data Engineering (ICDE’95). Taipei (1995) 3–14
Garofalakis, M., Rastogi, R., Shim, K.: Mining Sequential Patterns with Regular Expression Constraints. IEEE Trans. Knowl. Data Eng. 14 (2002) 530–552
Pei, J., Han, J., Wang, W.: Mining Sequential Patterns with Constraints in Large Databases. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM’02). McLean, VA (2002) 18–25
University of Waikato: Weka 3 — Data Mining with Open Source Machine Learning Software. http://www.cs.waikato.ac.nz/~ml/weka. (1999–2000)
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Lau, A., Ong, S.S., Mahidadia, A., Hoffmann, A., Westbrook, J., Zrimec, T. (2003). Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_13
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DOI: https://doi.org/10.1007/3-540-36175-8_13
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