Machine Learning and Knowledge Discovery in Databases

1. Front Matter

   Pages I-XXX

   PDF

2. Industrial Track

   1. Front Matter

      Pages 1-1

      PDF

   2. Autonomous HVAC Control, A Reinforcement Learning Approach

      • Enda Barrett, Stephen Linder

      Pages 3-19

   3. Clustering by Intent: A Semi-Supervised Method to Discover Relevant Clusters Incrementally

      • George Forman, Hila Nachlieli, Renato Keshet

      Pages 20-36

   4. Country-Scale Exploratory Analysis of Call Detail Records Through the Lens of Data Grid Models

      • Romain Guigourès, Dominique Gay, Marc Boullé, Fabrice Clérot, Fabrice Rossi

      Pages 37-52

   5. Early Detection of Fraud Storms in the Cloud

      • Hani Neuvirth, Yehuda Finkelstein, Amit Hilbuch, Shai Nahum, Daniel Alon, Elad Yom-Tov

      Pages 53-67

   6. Flexible Sliding Windows for Kernel Regression Based Bus Arrival Time Prediction

      • Hoang Thanh Lam, Eric Bouillet

      Pages 68-84

   7. Learning Detector of Malicious Network Traffic from Weak Labels

      • Vojtech Franc, Michal Sofka, Karel Bartos

      Pages 85-99

   8. Online Analysis of High-Volume Data Streams in Astroparticle Physics

      • Christian Bockermann, Kai Brügge, Jens Buss, Alexey Egorov, Katharina Morik, Wolfgang Rhode et al.

      Pages 100-115

   9. Robust Representation for Domain Adaptation in Network Security

      • Karel Bartos, Michal Sofka

      Pages 116-132

   10. Safe Exploration for Active Learning with Gaussian Processes

       • Jens Schreiter, Duy Nguyen-Tuong, Mona Eberts, Bastian Bischoff, Heiner Markert, Marc Toussaint

       Pages 133-149

   11. Semi-Supervised Consensus Clustering for ECG Pathology Classification

       • Helena Aidos, André Lourenço, Diana Batista, Samuel Rota Bulò, Ana Fred

       Pages 150-164

   12. Two Step graph-based semi-supervised Learning for Online Auction Fraud Detection

       • Phiradet Bangcharoensap, Hayato Kobayashi, Nobuyuki Shimizu, Satoshi Yamauchi, Tsuyoshi Murata

       Pages 165-179

   13. Watch-It-Next: A Contextual TV Recommendation System

       • Michal Aharon, Eshcar Hillel, Amit Kagian, Ronny Lempel, Hayim Makabee, Raz Nissim

       Pages 180-195

3. Nectar Track

   1. Front Matter

      Pages 197-197

      PDF

   2. Bayesian Hypothesis Testing in Machine Learning

      • Giorgio Corani, Alessio Benavoli, Francesca Mangili, Marco Zaffalon

      Pages 199-202

   3. Data-Driven Exploration of Real-Time Geospatial Text Streams

      • Harald Bosch, Robert Krüger, Dennis Thom

      Pages 203-207

   4. Discovering Neutrinos Through Data Analytics

      • Mathis Börner, Wolfgang Rhode, Tim Ruhe, for the IceCube Collaboration, Katharina Morik

      Pages 208-212

   5. Listener-Aware Music Recommendation from Sensor and Social Media Data

      • Markus Schedl

      Pages 213-217

   6. Logic-Based Incremental Process Mining

      • Stefano Ferilli, Domenico Redavid, Floriana Esposito

      Pages 218-221

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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