Principles of Data Mining and Knowledge Discovery: 6th by Kenji Abe, Shinji Kawasoe, Tatsuya Asai, Hiroki Arimura,

By Kenji Abe, Shinji Kawasoe, Tatsuya Asai, Hiroki Arimura, Setsuo Arikawa (auth.), Tapio Elomaa, Heikki Mannila, Hannu Toivonen (eds.)

This booklet constitutes the refereed complaints of the sixth ecu convention on rules of knowledge Mining and data Discovery, PKDD 2002, held in Helsinki, Finland in August 2002.
The 39 revised complete papers offered including four invited contributions have been conscientiously reviewed and chosen from a variety of submissions. one of the themes lined are kernel tools, probabilistic equipment, organization rule mining, tough units, sampling algorithms, development discovery, net textual content mining, meta info clustering, rule induction, info extraction, dependency detection, infrequent type prediction, classifier structures, textual content category, temporal series research, unsupervised studying, time sequence research, scientific facts mining, and so forth.

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Extra resources for Principles of Data Mining and Knowledge Discovery: 6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings

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Marden. Hypothesis testing: From p-values to Bayes factors. J. , 95:1316–1320, 2000. 31 17. G. Okugawa, G. Sedvall, M. Nordstr¨ om, N. C. Andreasen, R. Pierson, V. Magnotta, and I. Agartz. Selective reduction of the posterior superior vermis in men with chronic schizophrenia. Schizophrenia Research, (April), 2001. In press. 29 18. G. Sedvall and L. Terenius. In Schizophrenia: Pathophysiological mechanisms. Proceedings of the Nobel Symposium 111(1998) on Schizophrenia. Elsevier, 2000. 27 19. S. R.

A recent proposal is the control of false discovery rate[6]. Here we are only concerned that the rate (fraction) of false rejections is below a given level. If this rate is set to 5%, it means that of the rejected null hypotheses, on the average no Data Mining in Schizophrenia Research - Preliminary Analysis 31 more than 5% are falsely rejected. It was shown that if the tests are independent or positively correlated in a certain sense, one should truncate the rejection list at element k where k = max{i : pi ≤ qi/m}, m is the number of tests and pi is the ordered list of p-values.

The algorithm exhibited the same behavior on all the considered data set families. For the lack of space, we report only the experiments relative to the GAUSSIAN data set (see [2] for a detailed description). Figures 4 (b) and (c) show the execution times obtained respectively varying the dimensionality d and the size N of the data set. The curves show that the algorithm scales linearly both with respect to the dimensionality and the size of the data set. Figures 4 (d) and (e) report the execution times obtained varying the number n of top outliers and the type k of outliers respectively.

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