Abstract
Classification is an important data mining problem. Emerging Patterns (EPs) are itemsets whose supports change significantly from one data class to another. Previous studies have shown that classifiers based on EPs are competitive to other state-of-the-art classification systems. In this paper, we propose a new type of Emerging Patterns, called Maximal Emerging Patterns (MaxEPs), which are the longest EPs satisfying certain constraints. MaxEPs can be used to condense the vast amount of information, resulting in a significantly smaller set of high quality patterns for classification. We also develop a new “overlapping” or “intersection” based mechanism to exploit the properties of MaxEPs. Our new classifier, Classification by Maximal Emerging Patterns (CMaxEP), combines the advantages of the Bayesian approach and EP-based classifiers. The experimental results on 36 benchmark datasets from the UCI machine learning repository demonstrate that our method has better overall classification accuracy in comparison to JEP-classifier, CBA, C5.0 and NB.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Zaki, M.J., Ho, C.-T. (eds.) KDD 1999. LNCS (LNAI), vol. 1759, pp. 43–52. Springer, Heidelberg (1999)
Dong, G., Zhang, X., Wong, L., Li, J.: Classification by aggregating emerging patterns. In: Proc. 2nd Int’l Conf. on Discovery Science, pp. 30–42 (1999)
Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. Knowledge and Information Systems 3, 131–145 (2001)
Bailey, J., Manoukian, T., Ramamohanarao, K.: Fast algorithms for mining emerging patterns. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, p. 39. Springer, Heidelberg (2002)
Wang, Z.: Classification based on maximal contrast patterns. Master’s thesis, University of Melbourne (2004)
Domingos, P., Pazzani, M.J.: Beyond independence: Conditions for the optimality of the simple bayesian classifier. In: Proc. ICML 1996, pp. 105–112 (1996)
Meretakis, D., Wuthrich, B.: Extending naive bayes classifiers using long itemsets. In: Proc. KDD 1999, pp. 165–174 (1999)
Fan, H., Ramamohanarao, K.: A bayesian approach to use emerging patterns for classification. In: Proc. 14th Australasian Database Conference (ADC 2003), Adelaide, Australia, pp. 39–48 (2003)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. KDD 1998, New York, USA, pp. 80–86 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Z., Fan, H., Ramamohanarao, K. (2004). Exploiting Maximal Emerging Patterns for Classification. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_102
Download citation
DOI: https://doi.org/10.1007/978-3-540-30549-1_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
eBook Packages: Computer ScienceComputer Science (R0)