Abstract
In this work, we review an important kind of knowledge pattern, emerging patterns (EPs). Emerging patterns are associated with two data sets, and can be used to describe significant changes between the two data sets. To discover all EPs embedded in high-dimension and large-volume databases is a challenging problem due to the number of candidates. We describe a special type of EP, called jumping emerging patterns (JEPs) and review some properties of JEP spaces (the spaces of jumping emerging patterns). We describe efficient border-based algorithms to derive the boundary elements of JEP spaces. Moreover, we describe a new classifier, called DeEPs, which makes use of the discriminating power of emerging patterns. The experimental results show that the accuracy of DeEPs is much better than that of k-nearest neighbor and that of C5.0.
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References
R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM-SIGMOD International Conference on Management of Data, pages 207–216, Washington, D.C., May 1993. ACM Press.
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the Twentieth International Conference on Very Large Data Bases, pages 487–499, Santiago, Chile, September 1994.
Roberto J. Bayardo. Efficiently mining long patterns from databases. In Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data, pages 85–93. ACM Press, 1998.
C. L. Blake and P. M. Murphy. The UCI machine learning repository. [http://www.cs.uci.edu/~mlearn/MLRepository.html]. In Irvine, CA: University of California, Department of Information and Computer Science, 1998.
T. M. Cover and P. E. Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13:21–27, 1967.
Guozhu Dong and Jinyan Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 43–52, San Diego, CA, 1999. ACM Press.
Guozhu Dong, Xiuzhen Zhang, Limsoon Wong, and Jinyan Li. CAEP: Classification by aggregating emerging patterns. In Proceedings of the Second International Conference on Discovery Science, Tokyo, Japan, pages 30–42. Springer-Verlag, December 1999.
U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From data mining to knowledge discovery: An overview. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 1–34. AAAI/MIT Press, 1996.
Carl A. Gunter, Teow-Hin Ngair, and Devika Subramanian. The common ordertheoretic structure of version spaces and ATMS’s. In Artificial Intelligence, volume 95 of 2, pages 357–407, 1997.
Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao. Instance-based classification by emerging patterns. In Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases, page in press, Lyon, France, September 2000. Springer-Verlag.
Jinyan Li, Guozhu Dong, and Kotagiri Ramamohanarao. Making use of the most expressive jumping emerging patterns for classification. In Proceedings of the Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining. An expanded version of the paper was accepted by Knowledge and Information Systems: An International Journal, pages 220–232, Kyoto, Japan, April 2000. Springer-Verlag.
Jinyan Li, Kotagiri Ramamohanarao, and Guozhu Dong. The space of jumping emerging patterns and its incremental maintenance algorithms. In Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, USA, pages 551–558, San Francisco, June 2000. Morgan Kaufmann.
Jinyan Li, Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao, and Qun Sun. Efficient mining of high confidence association rules without support thresholds. In Proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 406–411, Prague, Czech Republic, September 1999. Springer-Verlag.
T. M. Mitchell. Version spaces: A candidate elimination approach to rule learning. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pages 305–310, Cambridge, MA, 1977.
T. M. Mitchell. Generalization as search. Artificial Intelligence, 18:203–226, 1982.
J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.
J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
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Li, J., Ramamohanarao, K., Dong, G. (2000). Emerging Patterns and Classification. In: Jifeng, H., Sato, M. (eds) Advances in Computing Science — ASIAN 2000. ASIAN 2000. Lecture Notes in Computer Science, vol 1961. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44464-5_3
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DOI: https://doi.org/10.1007/3-540-44464-5_3
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