6. PKDD 2002: Helsinki, Finland

Contributed Papers

• Kenji Abe, Shinji Kawasoe, Tatsuya Asai, Hiroki Arimura, Setsuo Arikawa:
  Optimized Substructure Discovery for Semi-structured Data. 1-14
  
• Fabrizio Angiulli, Clara Pizzuti:
  Fast Outlier Detection in High Dimensional Spaces. 15-26
  
• Stefan Arnborg, Ingrid Agartz, Håkan Hall, Erik Jönsson, Anna Sillén, Göran Sedvall:
  Data Mining in Schizophrenia Research - Preliminary Analysis. 27-38
  
• James Bailey, Thomas Manoukian, Kotagiri Ramamohanarao:
  Fast Algorithms for Mining Emerging Patterns. 39-50
  
• Christos Berberidis, Ioannis P. Vlahavas, Walid G. Aref, Mikhail J. Atallah, Ahmed K. Elmagarmid:
  On the Discovery of Weak Periodicities in Large Time Series. 51-61
  
• Damien Brain, Geoffrey I. Webb:
  The Need for Low Bias Algorithms in Classification Learning from Large Data Sets. 62-73
  
• Toon Calders, Bart Goethals:
  Mining All Non-derivable Frequent Itemsets. 74-85
  
• Yuta Choki, Einoshin Suzuki:
  Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance. 86-98
  
• Frans Coenen, Paul H. Leng:
  Finding Association Rules with Some Very Frequent Attributes. 99-111
  
• Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst D. Simon:
  Unsupervised Learning: Self-aggregation in Scaled Principal Component Space. 112-124
  
• Carlotta Domeniconi, Chang-Shing Perng, Ricardo Vilalta, Sheng Ma:
  A Classification Approach for Prediction of Target Events in Temporal Sequences. 125-137
  
• Amy P. Felty, Stan Matwin:
  Privacy-Oriented Data Mining by Proof Checking. 138-149
  
• George Forman:
  Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification. 150-162
  
• Dragan Gamberger, Nada Lavrac:
  Generating Actionable Knowledge by Expert-Guided Subgroup Discovery. 163-174
  
• Fosca Giannotti, Cristian Gozzi, Giuseppe Manco:
  Clustering Transactional Data. 175-187
  
• Shoji Hirano, Shusaku Tsumoto:
  Multiscale Comparison of Temporal Patternsin Time-Series Medical Databases. 188-199
  
• Eyke Hüllermeier:
  Association Rules for Expressing Gradual Dependencies. 200-211
  
• Szymon Jaroszewicz, Dan A. Simovici:
  Support Approximations Using Bonferroni-Type Inequalities. 212-224
  
• Baptiste Jeudy, Jean-François Boulicaut:
  Using Condensed Representations for Interactive Association Rule Mining. 225-236
  
• Mahesh V. Joshi, Ramesh C. Agarwal, Vipin Kumar:
  Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting. 237-249
  
• Hillol Kargupta, Krishnamoorthy Sivakumar, Samiran Ghosh:
  Dependency Detection in MobiMine and Random Matrices. 250-262
  
• Charles Kemp, Kotagiri Ramamohanarao:
  Long-Term Learning for Web Search Engines. 263-274
  
• Willi Klösgen, Michael May:
  Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database. 275-286
  
• Arno J. Knobbe, Arno Siebes, Bart Marseille:
  Involving Aggregate Functions in Multi-relational Search. 287-298
  
• Raymond Kosala, Jan Van den Bussche, Maurice Bruynooghe, Hendrik Blockeel:
  Information Extraction in Structured Documents Using Tree Automata Induction. 299-310
  
• Mehmet Koyutürk, Ananth Grama, Naren Ramakrishnan:
  Algebraic Techniques for Analysis of Large Discrete-Valued Datasets. 311-324
  
• Jinyan Li, Limsoon Wong:
  Geography of Differences between Two Classes of Data. 325-337
  
• Per Lidén, Lars Asker, Henrik Boström:
  Rule Induction for Classification of Gene Expression Array Data. 338-347
  
• Alexander Maedche, Valentin Zacharias:
  Clustering Ontology-Based Metadata in the Semantic Web. 348-360
  
• Hiroshi Mamitsuka:
  Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases. 361-372
  
• Gerhard Paass, Edda Leopold, Martha Larson, Jörg Kindermann, Stefan Eickeler:
  SVM Classification Using Sequences of Phonemes and Syllables. 373-384
  
• Laurence A. F. Park, Marimuthu Palaniswami, Kotagiri Ramamohanarao:
  A Novel Web Text Mining Method Using the Discrete Cosine Transform. 385-396
  
• Tobias Scheffer, Stefan Wrobel:
  A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases. 397-409
  
• Jun Sese, Shinichi Morishita:
  Answering the Most Correlated N Association Rules Efficiently. 410-422
  
• Shusaku Tsumoto:
  Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets aaand Medical Diagnostic Model. 423-434
  
• Adriano Veloso, Bruno Gusmão Rocha, Wagner Meira Jr., Márcio de Carvalho, Srinivasan Parthasarathy, Mohammed Javeed Zaki:
  Efficiently Mining Approximate Models of Associations in Evolving Databases. 435-448
  
• Robert Wall, Padraig Cunningham, Paul Walsh:
  Explaining Predictions from a Neural Network Ensemble One at a Time. 449-460
  
• Karsten Winkler, Myra Spiliopoulou:
  Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD. 461-474
  
• Djamel A. Zighed, Stéphane Lallich, Fabrice Muhlenbach:
  Separability Index in Supervised Learning. 475-487
  

Invited Papers

• Erkki Oja:
  Finding Hidden Factors UsingIndependent Component Analysis. 488
  
• Dan Roth:
  Reasoning with Classifiers. 489-493
  
• Bernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina S. Leslie, William Stafford Noble:
  A Kernel Approach for Learning from Almost Orthogonal Patterns. 494-511
  
• Padhraic Smyth:
  Learning with Mixture Models: Concepts and Applications. 512
  

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