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© 2020

New Frontiers in Mining Complex Patterns

8th International Workshop, NFMCP 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Revised Selected Papers

  • Michelangelo Ceci
  • Corrado Loglisci
  • Giuseppe Manco
  • Elio Masciari
  • Zbigniew Ras
Conference proceedings NFMCP 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11948)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11948)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Complex Patterns

    1. Front Matter
      Pages 1-1
    2. Len Feremans, Vincent Vercruyssen, Wannes Meert, Boris Cule, Bart Goethals
      Pages 3-20
  3. Classification and Regression

    1. Front Matter
      Pages 37-37
    2. Wojciech Jarmulski, Alicja Wieczorkowska
      Pages 39-51
    3. Ezgi Yıldırım, Payam Azad, Şule Gündüz Öğüdücü
      Pages 52-66
    4. Liana-Daniela Palcu, Marius Supuran, Camelia Lemnaru, Mihaela Dinsoreanu, Rodica Potolea, Raul Cristian Muresan
      Pages 67-82
  4. Streams and Times Series

    1. Front Matter
      Pages 83-83
    2. Shuai Wang, Mianwei Zhou, Sahisnu Mazumder, Bing Liu, Yi Chang
      Pages 100-115
  5. Applications

    1. Front Matter
      Pages 117-117
    2. Douglas Cirqueira, Markus Hofer, Dietmar Nedbal, Markus Helfert, Marija Bezbradica
      Pages 119-136
    3. Elżbieta Kubera, Alicja Wieczorkowska, Andrzej Kuranc
      Pages 137-154
  6. Back Matter
    Pages 155-155

About these proceedings

Introduction

This book constitutes the refereed post-conference proceedings of the 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with ECML-PKDD 2019 in Würzburg, Germany, in September 2019.
The workshop focused on the latest developments in the analysis of complex and massive data sources, such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.

Keywords

data mining machine learning multi-task learning supervised learning data stream mining clustering machine learning approaches ensemble methods artificial intelligence machine learning education engineering internetinternet learning signal processing computer hardware computer networks linguistics

Editors and affiliations

  1. 1.University of Bari Aldo MoroBariItaly
  2. 2.University of Bari Aldo MoroBariItaly
  3. 3.CNR-ICARRendeItaly
  4. 4.Federico II UniversityNaplesItaly
  5. 5.University of North CarolinaCharlotteUSA

Bibliographic information

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