A Novel Approach For Mining Emerging Patterns In Rare-Class Datasets
Mining emerging patterns (EPs) in rare-class databases is one of the new and difficult problems in knowledge discovery in databases (KDD). The main challenge in this task is the limited number of rare-class instances. This scarcity limits the number of emerging patterns that can be mined for the rare class. In this paper, we propose a novel approach for mining emerging patterns in rare-class datasets. We experimentally prove that our method is capable of gaining enough knowledge from the rare class; hence, it increases the performance of EP-based classifiers.
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- H. Alhammady, and K. Ramamohanarao. Expanding the Training Data Space Using Emerging Patterns and Genetic Methods. In Proceeding of the 2005 SIAM International Conference on Data Mining, New Port Beach, CA, USA.Google Scholar
- G. Dong, and J. Li. Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In Proceedings of the 1999 International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA.Google Scholar
- H. Alhammady, and K. Ramamohanarao. The Application of Emerging Patterns for Improving the Quality of Rare-class Classification. In Proceedings of the 2004 Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia.Google Scholar
- H. Alhammady, and K. Ramamohanarao. Using Emerging Patterns and Decision Trees in Rare-class Classification. In Proceedings of the 2004 IEEE International Conference on Data Mining, Brighton, UK.Google Scholar
- H. Fan, and K. Ramamohanarao. A Bayesian Approach to Use Emerging Patterns for Classification. In Proceedings of the 14th Australasian Database Conference (ADC’03), Adelaide, Australia.Google Scholar
- Guozhu D., Xiuzhen Z., Limsoon W., and Jinyan L.. CAEP: Classification by Aggregating Emerging Patterns. In Proceedings of the 2nd International Conference on Discovery Science (DS’99), Tokyo, Japan.Google Scholar
- C. Blake, E. Keogh, and C. J. Merz. UCI repository of machine learning databases. Department of Information and Computer Science, University of California at Irvine, CA, 1999. http://www.ics.uci.edu/∼ mlearn/MLRepository.html.Google Scholar
- H. Alhammady & K. Ramamohanarao. Mining Emerging Patterns and Classification in Data Streams. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI), Compiegne, France (Sep 2005), pp. 272-275.Google Scholar
- H. Alhammady & K. Ramamohanarao. Using Emerging Patterns to Construct Weighted Decision Trees. In IEEE Transactions on Knowledge and Data Engineering. Volume 18, Issue 7 (July 2006), pp. 865-876.Google Scholar
- Van Rijsbergan, C. J. (1979). Information retrieval. London, UK: Butterworths.Google Scholar
- Joshi, M. V., Agarwal, R. C., & Kumar, V. (2001). Mining needle in a haystack: classifying rare classes via two-phase rule induction. In Proceedings of the ACM-SIGMOD International Conference on Management of Data (ACM SIGMOD), Santa Barbara, CA, USA, pp. 91-102.Google Scholar