- {1} C. Blake and C. J. Merz. UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. {http://www.ics.uci.edu/~mlearn/MLRepository.html}., 1998.Google Scholar
- {2} W. Fan, W. Lee, S. Stolfo, and M. Miller. A multiple model cost-sensitive approach for intrusion detection. In Proceedings of the Eleventh European Conference on Machine Learning, 2000.Google ScholarDigital Library
- {3} E. Koegh and M. J. Pazzani. Scaling up dynamic time warping to massive datasets. In Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, 1999. Google ScholarDigital Library
- {4} P. Langley. Editorial: Machine learning as an experimental science. Machine Learning, 3(1):5-8, 1988. Google ScholarDigital Library
- {5} S. Lawrence, C. L. Giles, and K. Bollacker. Digital libraries and autonomous citation indexing. IEEE Computer , 32(6):67-71, 1999. Google ScholarDigital Library
- {6} D. Pavlov, H. Mannila, and P. Smyth. Probabilistic models for query approximation with large sparse binary data sets. In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, 2000. Google ScholarDigital Library
- {7} R. Ramakrishan and S. Stolfo, editors. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The Association for Computing Machinery, 2000. Google Scholar
- {8} S. Salzberg. On comparing classifiers: A critique of current research and methods. Data Mining and Knowledge Discovery, 1:1-12, 1999.Google Scholar
Index Terms
- The UCI KDD archive of large data sets for data mining research and experimentation
Recommendations
Mining uncertain data for constrained frequent sets
IDEAS '09: Proceedings of the 2009 International Database Engineering & Applications SymposiumData mining aims to search for implicit, previously unknown, and potentially useful pieces of information---such as sets of items that are frequently co-occurring together---that are embedded in data. The mined frequent sets can be used in the discovery ...
Mining fuzzy specific rare itemsets for education data
Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may ...
Extracting and Ingesting DDI Metadata and Digital Objects from a Data Archive into the iRODS Extension of the NARA TPAP Using the OAI-PMH
E-SCIENCE '09: Proceedings of the 2009 Fifth IEEE International Conference on e-ScienceThis prototype demonstrated that the migration of collections between digital libraries and preservation data archives is now possible using automated batch load for both data and metadata. We used this capability to enable collection interoperability ...
Comments