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By Eamonn J. Keogh, Michael J. Pazzani (auth.), Jan M. Żytkow, Jan Rauch (eds.)
This e-book constitutes the refereed lawsuits of the 3rd eu convention on rules and perform of information Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999.
The 28 revised complete papers and forty eight poster shows have been rigorously reviewed and chosen from 106 complete papers submitted. The papers are equipped in topical sections on time sequence, purposes, taxonomies and walls, good judgment equipment, dispensed and multirelational databases, textual content mining and have choice, ideas and induction, and fascinating and weird issues.
Read or Download Principles of Data Mining and Knowledge Discovery: Third European Conference, PKDD’99, Prague, Czech Republic, September 15-18, 1999. Proceedings PDF
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Extra resources for Principles of Data Mining and Knowledge Discovery: Third European Conference, PKDD’99, Prague, Czech Republic, September 15-18, 1999. Proceedings
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Environmental studies form an increasingly popular application domain for machine learning and data mining techniques. In this paper we consider two applications of decision tree learning in the domain of river water quality: a) the simultaneous prediction of multiple physico-chemical properties of the water from its biological properties using a single decision tree (as opposed to learning a different tree for each different property) and b) the prediction of past physico-chemical properties of the river water from its current biological properties.
Agrawal, K-I. S. Sawhney, K, Shim, Fast Similarity Search in the Presence of Noise, Scaling and Translation in Time Series Databases, in Proceedings of the 21 VLDB Conference, Z¨urich, (1995). 3. G. Das, D. Gunopulos, H. Mannila, Finding Similar Time Series, In Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial intelligence 1263, Springer, (1997). 4. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. , Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, (1996).
Thus, we introduce the extended averaging method to solve these two problems in the subsequent subsections. 2 Extended Moving Average for Continuous Attributes In this extension, we first focus on how moving average methods remove noise. The key idea is that a window parameter w is closely related with periodicity. If w is larger, then the periodical behavior whose time-constant is lower than w will be removed. Usually, a spike by noise is observed as a single event and this effect will be removed when w is taken as a large value.