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Machine Learning and Data Mining in Pattern Recognition

10th International Conference, MLDM 2014, St. Petersburg, Russia, July 21-24, 2014, Proceedings

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Petra Perner

    1. Institute of Computer Vision and Applied Computer Sciences, IBaI,, Leipzig, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8556)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: MLDM 2014.

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Table of contents (40 papers)

  1. Front Matter

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  2. Classification and Learning

    1. Efficient Error Setting for Subspace Miners

      • Eran Shaham, David Sarne, Boaz Ben-Moshe
      Pages 1-15

    2. Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier

      • Shunkai Fu, Sein Minn, Michel C. Desmarais
      Pages 16-30

    3. A Cost-Sensitive Based Approach for Improving Associative Classification on Imbalanced Datasets

      • Kitsana Waiyamai, Phoonperm Suwannarattaphoom
      Pages 31-42

    4. Multiple Regression Method Based on Unexpandable and Irreducible Convex Combinations

      • Oleg Senko, Alexander Dokukin
      Pages 43-57

    5. A Novel Approach for Identifying Banded Patterns in Zero-One Data Using Column and Row Banding Scores

      • Fatimah Binta Abdullahi, Frans Coenen, Russell Martin
      Pages 58-72

    6. Fast Pattern Recognition and Deep Learning Using Multi-Rooted Binary Decision Diagrams

      • Dmitry Bugaychenko, Dmitry Zubarevich
      Pages 73-77

    7. ACCD: Associative Classification over Concept-Drifting Data Streams

      • Kitsana Waiyamai, Thanapat Kangkachit, Bordin Saengthongloun, Thanawin Rakthanmanon
      Pages 78-90

    8. Integrating Weight with Ensemble to Handle Changes in Class Distribution

      • Nachai Limsetto, Kitsana Waiyamai
      Pages 91-106

    9. A Robust One-Class Classification Model with Interval-Valued Data Based on Belief Functions and Minimax Strategy

      • Lev V. Utkin, Yulia A. Zhuk, Anatoly I. Chekh
      Pages 107-118

    10. Manifold Learning in Data Mining Tasks

      • Alexander Kuleshov, Alexander Bernstein
      Pages 119-133

  3. Clustering

    1. Adaptive Multiple-Resolution Stream Clustering

      • Marwan Hassani, Pascal Spaus, Thomas Seidl
      Pages 134-148

    2. Monitoring Distributed Data Streams through Node Clustering

      • Maria Barouti, Daniel Keren, Jacob Kogan, Yaakov Malinovsky
      Pages 149-162

    3. Minimizing Cluster Errors in LP-Based Nonlinear Classification

      • Orestes G. Manzanilla-Salazar, Jesús Espinal-Kohler, Ubaldo M. García-Palomares
      Pages 163-174

  4. Outlier Detection

    1. CBOF: Cohesiveness-Based Outlier Factor A Novel Definition of Outlier-ness

      • Vineet Joshi, Raj Bhatnagar
      Pages 175-189

    2. A New Measure of Outlier Detection Performance

      • Kliton Andrea, Georgy Shevlyakov, Natalia Vassilieva, Alexander Ulanov
      Pages 190-197

  5. Hierarchical Classification

    1. A Multi-path Strategy for Hierarchical Ensemble Classification

      • Esra’a Alshdaifat, Frans Coenen, Keith Dures
      Pages 198-212

    2. Label Correction Strategy on Hierarchical Multi-Label Classification

      • Thanawut Ananpiriyakul, Piyapan Poomsirivilai, Peerapon Vateekul
      Pages 213-227

  6. Time Series and Sequential Pattern Mining

    1. Towards Time Series Classification without Human Preprocessing

      • Patrick Schäfer
      Pages 228-242

    2. Applications of Concurrent Sequential Patterns in Protein Data Mining

      • Cuiqing Wang, Jing Lu, Malcolm Keech
      Pages 243-257
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About this book

This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Editors and Affiliations

  • Institute of Computer Vision and Applied Computer Sciences, IBaI,, Leipzig, Germany

    Petra Perner

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