Abstract
The paper proposes a way of mining peculiarity rules from multiply statistical and transaction databases. We introduce the peculiarity rules as a new type of association rules, which can be discovered from a relatively small number of the peculiar data by searching the relevance among the peculiar data. We argue that the peculiarity rules represent a typically unexpected, interesting regularity hidden in statistical and transaction databases. We describe how to mine the peculiarity rules in the multi-database environment and how to use the RVER (Reverse Variant Entity-Relationship) model to represent the result of multi-database mining. Our approach is based on the database reverse engineering methodology and granular computing techniques.
Chapter PDF
Similar content being viewed by others
Keywords
References
Agrawal, R., et al.: Database Mining: A Performance Perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925 (1993)
Agrawal, R., et al.: Fast Discovery of Association Rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)
Aronis, J.M., et al.: The WoRLD; Knowledge Discovery from Multiple Distributed Databases. In: Proc. 10th International Florida Al Research Symposium (FLAIRS 1997), pp. 337–341 (1997)
Fayyad, U.M., Piatetsky-Shapiro, G., et al. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park (1996)
Freitas, A.A.: On Objective Measures of Rule Surprisingness. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS (LNAI), vol. 1510, pp. 1–9. Springer, Heidelberg (1998)
Chiang, R.H.L., et al.(eds.): A Framework for the Design and Evaluation of Reverse Engineering Methods for Relational Databases. Data & Knowledge Engineering 21, 57–77 (1997)
Lin, T.Y.: Granular Computing on Binary Relations 1: Data Mining and Neighborhood Systems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1. Studies in Fuzziness and Soft Computing series, vol. 18, pp. 107–121. Physica-Verlag, Heidelberg (1998)
Lin, T.Y., Zhong, N., Dong, J., Ohsuga, S.: Frameworks for Mining Binary Relations in Data. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 387–393. Springer, Heidelberg (1998)
Liu, B., Hsu, W., Chen, S.: Using General Impressions to Analyze Discovered Classification Rules. In: Proc. Third International Conference on Knowledge Discovery and Data Mining (KDD 1997), pp. 31–36. AAAI Press, Menlo Park (1997)
Liu, H., Lu, H., Yao, J.: Identifying Relevant Databases for Multidatabase Mining. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS (LNAI), vol. 1394, pp. 210–221. Springer, Heidelberg (1998)
Ribeiro, J.S., Kaufman, K.A., Kerschberg, L.: Knowledge Discovery from Multiple Databases. In: Proc. First Inter. Conf. on Knowledge Discovery and Data Mining (KDD 1995), pp. 240–245. AAAI Press, Menlo Park (1995)
Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Trans. Knowl. Data Eng. 8(6), 970–974 (1996)
Suzuki, E.: Autonomous Discovery of Reliable Exception Rules. In: Proc Third Inter. Conf. on Knowledge Discovery and Data Mining (KDD 1997), pp. 259–262. AAAI Press, Menlo Park (1997)
Wrobel, S.: An Algorithm for Multi-relational Discovery of Subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS (LNAI), vol. 1263, pp. 367–375. Springer, Heidelberg (1997)
Yao, Y.Y.: Granular Computing using Neighborhood Systems. In: Roy, R., Furuhashi, T., Chawdhry, P.K. (eds.) Advances in Soft Computing: Engineering Design and Manufacturing, pp. 539–553. Springer, Heidelberg (1999)
Yao, Y.Y., Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 479–488. Springer, Heidelberg (1999)
Yao, Y.Y., Zhong, N.: Potential Applications of Granular Computing in Knowledge Discovery and Data Mining. In: Proc. The 5th International Conference on Information Systems Analysis and Synthesis, IASA 1999 edited in the invited session on Intelligent Data Mining and Knowledge Discovery (1999) (in press)
Zadeh, L.A.: Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90, 111–127 (1997)
Zhong, N., Yamashita, S.: A Way of Multi-Database Mining. In: Proc. the IASTED International Conference on Artificial Intelligence and Soft Computing (ASC 1998), pp. 384–387. IASTED/ACTA Press (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhong, N., Yao, Y.Y., Ohsuga, S. (1999). Peculiarity Oriented Multi-database Mining. In: Żytkow, J.M., Rauch, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science(), vol 1704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48247-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-48247-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66490-1
Online ISBN: 978-3-540-48247-5
eBook Packages: Springer Book Archive