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Additional resources for Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing)
Org/TR/SOAP/, May 2000. , and Roddick, J. , Higher order mining: Modelling and mining the results of knowledge discovery, Data Mining II – Proceedings of the 2nd International Conference on Data Mining Methods and Databases, Cambridge, UK, pp. 309–20, 2000. asp, 2000. pdf, 2001. , Sampling large databases for association rules, Proceedings of the 22nd International Conference on Very Large Data Bases, San Francisco, pp. 134– 45, 1996. org/, 2001. , CRISP-DM: Towards a standard process model for data mining, Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pp.
Anomaly or novelty detection, and nonobvious multivariate dependencies of the involved parameters and the structure in the data must be disclosed for improved process control. , from soft computing, for online observation and oﬄine interactive analysis employing novelty clas- 36 Andreas K¨ onig and Achim Gratz siﬁcation, dimensionality reduction, and interactive data visualization techniques can be employed. 4 Electrical Test Data After fabrication, electrical tests (ET) on the wafer level are carried out to assess that all single devices deﬁned by the process are within their speciﬁed range.
And Uthurusamy, R. (eds), Advances in Knowledge Discovery and Data Mining, 22 Krzysztof J. Cios and Lukasz A. Kurgan AAAI/MIT Press, 1996. , Scaling clustering algorithms to large databases, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, Menlo Park, CA, pp. 9–15, 1998. org/TR/2000/REC-xml-20001006, October 2000. , Friedman, J. , Olshen, R. , and Stone, C. , Classification and Regression Trees, Wadsworth Int. Group, Belmont, CA, 1984. , Fernandez, M. , UnQL: A query language and algebra for semistructured data based on structural recursion, Very Large Databases Journal, 9(1), pp.