Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, ProceedingsNada Lavrač, Dragan Gamberger, Hendrik Blockeel, Ljupco Todorovski Springer, 2003 M11 18 - 512 pages The proceedings of ECML/PKDD2003 are published in two volumes: the P- ceedings of the 14th European Conference on Machine Learning (LNAI 2837) and the Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2838). The two conferences were held on September 22–26, 2003 in Cavtat, a small tourist town in the vicinity of Dubrovnik, Croatia. As machine learning and knowledge discovery are two highly related ?elds, theco-locationofbothconferencesisbene?cialforbothresearchcommunities.In Cavtat, ECML and PKDD were co-located for the third time in a row, following the successful co-location of the two European conferences in Freiburg (2001) and Helsinki (2002). The co-location of ECML2003 and PKDD2003 resulted in a joint program for the two conferences, including paper presentations, invited talks, tutorials, and workshops. Out of 332 submitted papers, 40 were accepted for publication in the ECML2003proceedings,and40wereacceptedforpublicationinthePKDD2003 proceedings. All the submitted papers were reviewed by three referees. In ad- tion to submitted papers, the conference program consisted of four invited talks, four tutorials, seven workshops, two tutorials combined with a workshop, and a discovery challenge. |
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Page 9
... accurate predictions and also give important insights into the structure of the data the algorithm is processing. The main example I discuss is RF/tools, a collection of algorithms for classification, regression and multiple dependent ...
... accurate predictions and also give important insights into the structure of the data the algorithm is processing. The main example I discuss is RF/tools, a collection of algorithms for classification, regression and multiple dependent ...
Page 15
... Accurate modeling of region data. IEEE TKDE, 13(6):874–883, November 2001. M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In SIGKDD, pages 61–70, Edmonton, Canada, 2002. M. Ripeanu, I. Foster, and A ...
... Accurate modeling of region data. IEEE TKDE, 13(6):874–883, November 2001. M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In SIGKDD, pages 61–70, Edmonton, Canada, 2002. M. Ripeanu, I. Foster, and A ...
Page 24
... accuracy would not be significant if it only covers 2 customers in a large database, and so would not be interesting. This aspect of interestingness is sometimes referred to as the reliability of a rule [11]. In many association rule ...
... accuracy would not be significant if it only covers 2 customers in a large database, and so would not be interesting. This aspect of interestingness is sometimes referred to as the reliability of a rule [11]. In many association rule ...
Page 31
... -values, the seeded motif is reported less often if it is relatively infrequent (larger motif sizes) or less predictive (lower odds). Fig. 2. Variation of accuracy with varying δ. Efficient Statistical Pruning of Association Rules 31.
... -values, the seeded motif is reported less often if it is relatively infrequent (larger motif sizes) or less predictive (lower odds). Fig. 2. Variation of accuracy with varying δ. Efficient Statistical Pruning of Association Rules 31.
Page 34
... accurate computation of the hypergeometric distribution function. ACM Transactions on Mathematical Software (TOMS), 19(1):33–43, 1993. M. Zaki. Generating non-redundant association rules. In KDD 2000, pages 34–43, 2000. Majority ...
... accurate computation of the hypergeometric distribution function. ACM Transactions on Mathematical Software (TOMS), 19(1):33–43, 1993. M. Zaki. Generating non-redundant association rules. In KDD 2000, pages 34–43, 2000. Majority ...
Contents
1 | |
16 | |
35 | |
Anticipated Data Reduction in Constrained Pattern Mining | 59 |
Discovering Unbounded Episodes in Sequential Data | 83 |
Improving Prediction of the Minority Class in Boosting | 107 |
Using Belief Networks and Fisher Kernels | 120 |
On Decision Boundaries of Naıve Bayes in Continuous Domains | 144 |
Using Transduction and Multiview Learning to Answer Emails | 266 |
Exploring Fringe Settings of SVMs for Classification | 278 |
Rule Discovery and Probabilistic Modeling for Onomastic Data | 291 |
Symbolic Distance Measurements Based on Characteristic Subspaces | 315 |
Collaborative Filtering Using Restoration Operators | 339 |
Statistical σPartition Clustering over Data Streams | 387 |
Text Categorisation Using Document Profiling | 411 |
BottomUp Learning of Logic Programs for Information Extraction | 435 |
Visualizing Class Probability Estimators | 168 |
An IndiscernibilityBased Clustering Method with Iterative Refinement | 192 |
Explaining Text Clustering Results Using Semantic Structures | 217 |
Ranking Interesting Subspaces for Clustering High Dimensional Data | 241 |
Mining Rules of Multilevel Diagnostic Procedure from Databases | 459 |
Topic Learning from Few Examples | 483 |
Author Index | 507 |
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Common terms and phrases
accuracy anti-monotone applications approach Artificial Intelligence association rules attributes Bayesian Bayesian classifier Bayesian networks cell classifier clustering collaborative filtering computed concept consider data elements Data Mining database dataset decision tree defined denote density detection discretization distribution document domain efficient episodes equivalence relations estimation evaluation example Figure function fuzzy given graph induction Inductive Logic Programming input instance interaction itemsets km found Knowledge Discovery labeled LNAI Machine Learning method mining algorithms minority class monotone constraint Na¨ıve Naive Bayes node objects occurrence optimal P-tree p-value parameters partition pattern mining performance pharmacophore PKDD prediction preference probability problem Proc propensity score proposed pruning query relation representation rule induction sample search space Section sequence skeleton SMOTE statistical strategy structure subset subspaces support vector machine Table target technique threshold topic tree values variables vector