New Frontiers in Mining Complex Patterns

1. Front Matter

   Pages I-XII

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2. Classification and Regression

   1. Front Matter

      Pages 1-1

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   2. Semi-supervised Learning for Multi-target Regression

      • Jurica Levatić, Michelangelo Ceci, Dragi Kocev, Sašo Džeroski

      Pages 3-18

   3. Evaluation of Different Data-Derived Label Hierarchies in Multi-label Classification

      • Gjorgji Madjarov, Ivica Dimitrovski, Dejan Gjorgjevikj, Sašo Džeroski

      Pages 19-37

3. Clustering

   1. Front Matter

      Pages 39-39

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   2. Predicting Negative Side Effects of Surgeries Through Clustering

      • Ayman Hajja, Hakim Touati, Zbigniew W. Raś, James Studnicki, Alicja A. Wieczorkowska

      Pages 41-55

   3. Parallel Multicut Segmentation via Dual Decomposition

      • Julian Yarkony, Thorsten Beier, Pierre Baldi, Fred A. Hamprecht

      Pages 56-68

   4. Learning from Imbalanced Data Using Ensemble Methods and Cluster-Based Undersampling

      • Parinaz Sobhani, Herna Viktor, Stan Matwin

      Pages 69-83

4. Data Streams and Sequences

   1. Front Matter

      Pages 85-85

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   2. Prequential AUC for Classifier Evaluation and Drift Detection in Evolving Data Streams

      • Dariusz Brzezinski, Jerzy Stefanowski

      Pages 87-101

   3. Mining Positional Data Streams

      • Jens Haase, Ulf Brefeld

      Pages 102-116

   4. Visualization for Streaming Telecommunications Networks

      • Rui Sarmento, Mário Cordeiro, João Gama

      Pages 117-131

   5. Temporal Dependency Detection Between Interval-Based Event Sequences

      • Marc Plantevit, Vasile-Marian Scuturici, Céline Robardet

      Pages 132-146

5. Applications

   1. Front Matter

      Pages 147-147

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   2. Discovering Behavioural Patterns in Knowledge-Intensive Collaborative Processes

      • Claudia Diamantini, Laura Genga, Domenico Potena, Emanuele Storti

      Pages 149-163

   3. Learning Complex Activity Preconditions in Process Mining

      • Stefano Ferilli, Berardina De Carolis, Floriana Esposito

      Pages 164-178

   4. Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support Thresholds

      • Ilkcan Keles, Mert Ozer, I. Hakki Toroslu, Pinar Karagoz

      Pages 179-193

   5. Pitch-Related Identification of Instruments in Classical Music Recordings

      • Elżbieta Kubera, Alicja A. Wieczorkowska

      Pages 194-209

6. Back Matter

   Pages 211-211

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This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2014, held in conjunction with ECML-PKDD 2014 in Nancy, France, in September 2014. The 13 revised full papers presented were carefully reviewed and selected from numerous submissions. They illustrate advanced data mining techniques which preserve the informative richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: classification and regression; clustering; data streams and sequences; applications.

• classification
• clustering
• data mining
• feature selection
• machine learning
• network models
• semantic similarity measures
• support vector machines
• time series analysis
• validation

• Università degli Studi di Bari Aldo Moro, Bari, Italy

  Annalisa Appice, Michelangelo Ceci, Corrado Loglisci

• ICAR-CNR, Rende, Italy

  Giuseppe Manco, Elio Masciari

• University of North Carolina, Charlotte, USA and Warsaw University of Technology, Warsaw, Poland

  Zbigniew W. Ras

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