Abstract
In this paper, we discuss an approach for discovering temporal changes in event sequences, and present first results from a study on demographic data. The data encode characteristic events in a person’s life course, such as their birth date, the begin and end dates of their partnerships and marriages, and the birth dates of their children. The goal is to detect significant changes in the chronology of these events over people from different birth cohorts. To solve this problem, we encoded the temporal information in a first-order logic representation, and employed Warmr, an ILP system that discovers association rules in a multi-relational data set, to detect frequent patterns that show significant variance over different birth cohorts. As a case study in multi-relational association rule mining, this work illustrates the flexibility resulting from the use of first-order background knowledge, but also uncovers a number of important issues that hitherto received little attention.
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R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, 1995.
R. Agrawal, G. Psaila, E. L. Wimmers, and M. Zaït. Querying shapes of histories. In Proceedings of the 21st Conference on Very Large Databases (VLDB-95), Zurich, Switzerland, 1995.
R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of the International Conference on Data Engineering (ICDE), Taipei, Taiwan, 1995.
S. D. Bay and M. J. Pazzani. Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery, 2001. To appear.
R. J. Bayardo Jr. and R. Agrawal. Mining the Most Interesting Rules. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 145–154, 1999.
H. Blockeel, L. Dehaspe, B. Demoen, G. Janssens, J. Ramon, and H. Vandecasteele. Executing query packs in ILP. In J. Cussens and A. Frisch (eds.) Proceedings of the 10th International Conference on Inductive Logic Programming (ILP-2000), pp. 60–77, London, UK, 2000. Springer-Verlag.
J.-F. Boulicaut and A. Bykowski. Frequent closures as a concise representation for binary data mining. In Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-00), pp. 62–73, Kyoto, Japan. Springer-Verlag.
S. Chakrabarti, S. Sarawagi, and B. Dom. Mining surprising patterns using temporal description length. In Proceedings of the 24th Conference on Very Large Databases (VLDB-98), New York, NY, 1998.
L. Dehaspe and H. Toivonen. Discovery of frequent Datalog patterns. Data Mining and Knowledge Discovery, 3(1):7–36, 1999.
S. Džeroski and N. Lavrač (eds.). Relational Data Mining: Inductive Logic Programming for Knowledge Discovery in Databases. Springer-Verlag, 2001. To appear.
J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for association rule mining — a general survey and comparison. SIGKDD explorations, 2(1):58–64, June 2000.
B. Lent, R. Agrawal, and R. Srikant. Discovering trends in text databases. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, CA, 1997.
H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259–289, 1997.
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proceedings of the 5th International Conference on Extending Database Technology (EDBT), Avignon, France, 1996.
L. J. G. van Wissen and P. A. Dykstra (eds.). Population Issues: An Interdisciplinary Focus. Kluwer Academic/Plenum Publishers, New York, 1999.
G. I. Webb. Efficient search for association rules. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000), pp. 99–107, Boston, MA, 2000.
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Blockeel, H., Fürnkranz, J., Prskawetz, A., Billari, F.C. (2001). Detecting Temporal Change in Event Sequences: An Application to Demographic Data. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_3
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DOI: https://doi.org/10.1007/3-540-44794-6_3
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