Machine Learning and Knowledge Discovery in Databases

1. Nectar Track

   1. Mobility Mining for Journey Planning in Rome

      • Michele Berlingerio, Veli Bicer, Adi Botea, Stefano Braghin, Nuno Lopes, Riccardo Guidotti et al.

      Pages 222-226

   2. Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing

      • Mehdi Kaytoue, Victor Codocedo, Aleksey Buzmakov, Jaume Baixeries, Sergei O. Kuznetsov, Amedeo Napoli

      Pages 227-231

   3. Privacy Preserving Blocking and Meta-Blocking

      • Alexandros Karakasidis, Georgia Koloniari, Vassilios S. Verykios

      Pages 232-236

   4. Social Data Mining and Seasonal Influenza Forecasts: The FluOutlook Platform

      • Qian Zhang, Corrado Gioannini, Daniela Paolotti, Nicola Perra, Daniela Perrotta, Marco Quaggiotto et al.

      Pages 237-240

   5. Star Classification Under Data Variability: An Emerging Challenge in Astroinformatics

      • Ricardo Vilalta, Kinjal Dhar Gupta, Ashish Mahabal

      Pages 241-244

   6. The Evolution of Social Relationships and Strategies Across the Lifespan

      • Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang

      Pages 245-249

   7. Understanding Where Your Classifier Does (Not) Work

      • Wouter Duivesteijn, Julia Thaele

      Pages 250-253

   8. Visual Analytics Methodology for Scalable and Privacy-Respectful Discovery of Place Semantics from Episodic Mobility Data

      • Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Piotr Jankowski

      Pages 254-258

   9. Will This Paper Increase Your h-index?

      • Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

      Pages 259-263

2. Demo Track

   1. Front Matter

      Pages 265-265

      PDF

   2. CardioWheel: ECG Biometrics on the Steering Wheel

      • André Lourenço, Ana Priscila Alves, Carlos Carreiras, Rui Policarpo Duarte, Ana Fred

      Pages 267-270

   3. Data Patterns Explained with Linked Data

      • Ilaria Tiddi, Mathieu d’Aquin, Enrico Motta

      Pages 271-275

   4. Gazouille: Detecting and Illustrating Local Events from Geolocalized Social Media Streams

      • Pierre Houdyer, Albrecht Zimmerman, Mehdi Kaytoue, Marc Plantevit, Joseph Mitchell, Céline Robardet

      Pages 276-280

   5. HiDER: Query-Driven Entity Resolution for Historical Data

      • Bijan Ranjbar-Sahraei, Julia Efremova, Hossein Rahmani, Toon Calders, Karl Tuyls, Gerhard Weiss

      Pages 281-284

   6. IICE: Web Tool for Automatic Identification of Chemical Entities and Interactions

      • Andre Lamurias, Luka A. Clarke, Francisco M. Couto

      Pages 285-288

   7. Interactively Exploring Supply and Demand in the UK Independent Music Scene

      • Matt McVicar, Cédric Mesnage, Jefrey Lijffijt, Tijl De Bie

      Pages 289-292

   8. Monitoring Production Equipment in Remote Locations

      • Ujval Kamath, Ana Costa e Silva, Michael O’Connell

      Pages 293-297

   9. MultiNets: Web-Based Multilayer Network Visualization

      • Matija Piškorec, Borut Sluban, Tomislav Šmuc

      Pages 298-302

   10. OMEGA: An Order-Preserving SubMatrix Mining, Indexing and Search Tool

       • Tao Jiang, Zhanhuai Li, Qun Chen, Zhong Wang, Kaiwen Li, Wei Pan

       Pages 303-307

   11. Probabilistic Programming in Anglican

       • David Tolpin, Jan-Willem van de Meent, Frank Wood

       Pages 308-311

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

• data mining
• foundations of machine learning and data mining
• knowledge discovery in databases
• probabilistic models and statistical methods
• social and graphs mining
• classification, regression and supervised learning
• clustering and unsupervised learning
• domain adaptation
• ensemble learning
• large scale learning and big data
• learning paradigms
• machine learning and data mining applications
• machine learning methodologies
• meta-learning
• nonmonotonic constraints
• pattern and sequence mining
• privacy-preserving data mining
• probabilistic programming
• recommender systems
• rich data mining

• Huawei Noah’s Ark Lab, Shatin, Hong Kong

  Albert Bifet

• Siemens AG Corporate Technology, München, Germany

  Michael May

• IBM Research Brazil, Rio de Janeiro, Brazil

  Bianca Zadrozny

• Universitat Politècnica de Catalunya, Barcelona, Spain

  Ricard Gavalda

• Università di Pisa, Pisa, Italy

  Dino Pedreschi

• Eurecat / Yahoo Labs, Barcelona, Spain

  Francesco Bonchi

• University of Porto - INESC TEC, Porto, Portugal

  Jaime Cardoso

• Otto-von-Guericke University, Magdeburg, Germany

  Myra Spiliopoulou

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