This book constitutes the refereed proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2004, held in Pisa, Italy, in September 2004 jointly with ECML 2004. The 39 revised full papers and 9 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 194 papers submitted to PKDD and 107 papers submitted to both, PKDD and ECML. The papers present a wealth of new results in knowledge discovery in databases and address all current issues in the area.
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Given a collection of n data points (vectors) in high-dimensional Euclidean space it is natural to ask whether they can be projected into a lower dimensional Euclidean space without suffering great distortion. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
thick skyline, skyline objects, optimal matching tree, sparse bigrams, maximum gap constraint, combinatorial tiles, regression nomograms, suitable local representatives, cosine criterion, negative association rules, hierarchical clustering engine, compressed continuities, feature mapping function, checker filter, window size constraint, functionality interchanges, hierarchical tiles, blosum scores, classified clustering, position invariant features, multiscale matching, size regularization, chemical reaction databases, pattern query language, spatial association rules Key Phrases - Capitalized Phrases (CAPs): (learn more)
Springer-Verlag Berlin Heidelberg, Artificial Intelligence, New York, Morgan Kaufmann, Data Engineering, Evidence Visualizer, John Wiley, Very Large Data Bases, Computer Society, European Conference, Geographical Information Systems, Annalisa Appice, Aristides Gionis, Camilo Rostoker, Density-Based Spatial Clustering, Hans-Peter Kriegel, Martin Pfeifle, Michael Egmont-Petersen, Symptom Complex, Xin Wang, Adriano Veloso, Analysing Customer Churn, Heikki Mannila, Nan Zhang, Shengquan Wang New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Given a collection of n data points (vectors) in high-dimensional Euclidean space it is natural to ask whether they can be projected into a lower dimensional Euclidean space without suffering great distortion. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
thick skyline, skyline objects, optimal matching tree, sparse bigrams, maximum gap constraint, combinatorial tiles, regression nomograms, suitable local representatives, cosine criterion, negative association rules, hierarchical clustering engine, compressed continuities, feature mapping function, checker filter, window size constraint, functionality interchanges, hierarchical tiles, blosum scores, classified clustering, position invariant features, multiscale matching, size regularization, chemical reaction databases, pattern query language, spatial association rules Key Phrases - Capitalized Phrases (CAPs): (learn more)
Springer-Verlag Berlin Heidelberg, Artificial Intelligence, New York, Morgan Kaufmann, Data Engineering, Evidence Visualizer, John Wiley, Very Large Data Bases, Computer Society, European Conference, Geographical Information Systems, Annalisa Appice, Aristides Gionis, Camilo Rostoker, Density-Based Spatial Clustering, Hans-Peter Kriegel, Martin Pfeifle, Michael Egmont-Petersen, Symptom Complex, Xin Wang, Adriano Veloso, Analysing Customer Churn, Heikki Mannila, Nan Zhang, Shengquan Wang New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 72
books:
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- Pattern Classification (2nd Edition) by Richard O. Duda on 5 pages
- on 5 pages
- Machine Learning by Tom M. Mitchell on 4 pages
- Data Mining by Jiawei Han on page 98, page 123, and page 517
- Science and Technology (Legacies) by Julian Rowe on page 347, page 398, and page 469
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