New Frontiers in Mining Complex Patterns

4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers

  • Michelangelo Ceci
  • Corrado Loglisci
  • Giuseppe Manco
  • Elio Masciari
  • Zbigniew W. Ras
Conference proceedings NFMCP 2015
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9607)

Table of contents

  1. Front Matter
    Pages I-X
  2. Data Stream Mining

    1. Front Matter
      Pages 1-1
    2. Aljaž Osojnik, Panče Panov, Sašo Džeroski
      Pages 17-31
    3. Lázaro Bustio, René Cumplido, Raudel Hernández, José M. Bande, Claudia Feregrino
      Pages 32-45
    4. Annalisa Appice, Marco Di Pietro, Claudio Greco, Donato Malerba
      Pages 46-60
    5. M. Kehinde Olorunnimbe, Herna L. Viktor, Eric Paquet
      Pages 61-75
  3. Classification

    1. Front Matter
      Pages 91-91
    2. Bettina Fazzinga, Sergio Flesca, Filippo Furfaro, Luigi Pontieri
      Pages 108-124
    3. Matej Mihelčić, Sašo Džeroski, Nada Lavrač, Tomislav Šmuc
      Pages 125-143
  4. Mining Complex Data

    1. Front Matter
      Pages 145-145
    2. Fabio Leuzzi, Stefano Ferilli
      Pages 147-162
    3. Alicja Wieczorkowska, Elżbieta Kubera, Tomasz Słowik, Krzysztof Skrzypiec
      Pages 163-178
    4. Pascal Welke, Tamás Horváth, Stefan Wrobel
      Pages 179-193
    5. Jan Kralj, Marko Robnik-Šikonja, Nada Lavrač
      Pages 194-208
  5. Sequences

    1. Front Matter
      Pages 209-209
    2. Zhao Xu, Koichi Funaya, Haifeng Chen, Sergio Leoni
      Pages 211-223

About these proceedings

Introduction

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2015, held in conjunction with ECML-PKDD 2015 in Porto, Portugal, in September 2015.

The 15 revised full papers presented together with  one invited talk were carefully reviewed and selected from 19 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: data stream mining, classification, mining complex data, and sequences.

Keywords

data mining ensemble methods knowledge discovery machine learning parallel algorithms classification clustering data stream mining feature selection first-order logic logic programming metalearning multi-task learning network analysis semantic similarity measures supervised learning support vector machines text mining heuristics time series analysis validation

Editors and affiliations

  • Michelangelo Ceci
    • 1
  • Corrado Loglisci
    • 2
  • Giuseppe Manco
    • 3
  • Elio Masciari
    • 4
  • Zbigniew W. Ras
    • 5
  1. 1.Università degli Studi di Bari Aldo MoroBariItaly
  2. 2.Università degli Studi di Bari Aldo MoroBariItaly
  3. 3.ICAR-CNRRendeItaly
  4. 4.ICAR-CNRRendeItaly
  5. 5.University of North CarolinaCharlotteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-39315-5
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-39314-8
  • Online ISBN 978-3-319-39315-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349