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Machine Learning and Data Mining in Pattern Recognition

11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings

  • Petra Perner
Conference proceedings MLDM 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9166)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9166)

Table of contents

  1. Front Matter
    Pages I-IX
    PDF

  2. Graph Mining

    1. Front Matter
      Pages 1-1
      PDF
    2. Greedy Graph Edit Distance
      Kaspar Riesen, Miquel Ferrer, Rolf Dornberger, Horst Bunke
      Pages 3-16
    3. Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation
      Miquel Ferrer, Francesc Serratosa, Kaspar Riesen
      Pages 17-31
    4. Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning
      Nimit Dhulekar, Srinivas Nambirajan, Basak Oztan, Bülent Yener
      Pages 32-52

  3. Classification and Regression

    1. Front Matter
      Pages 53-53
      PDF
    2. Local and Global Genetic Fuzzy Pattern Classifiers
      Søren Atmakuri Davidsen, E. Sreedevi , M. Padmavathamma
      Pages 55-69
    3. IKLTSA: An Incremental Kernel LTSA Method
      Chao Tan, Jihong Guan, Shuigeng Zhou
      Pages 70-83

  4. Sentiment Analysis

    1. Front Matter
      Pages 85-85
      PDF
    2. SentiSAIL: Sentiment Analysis in English, German and Russian
      Gayane Shalunts, Gerhard Backfried
      Pages 87-97
    3. Sentiment Analysis for Government: An Optimized Approach
      Angelo Corallo, Laura Fortunato, Marco Matera, Marco Alessi, Alessio Camillò, Valentina Chetta et al.
      Pages 98-112

  5. Data Preparation and Missing Values

    1. Front Matter
      Pages 113-113
      PDF
    2. A Novel Algorithm for the Integration of the Imputation of Missing Values and Clustering
      Roni Ben Ishay, Maya Herman
      Pages 115-129
    3. Improving the Algorithm for Mapping of OWL to Relational Database Schema
      Chien D. C. Ta, Tuoi Phan Thi
      Pages 130-139
    4. Robust Principal Component Analysis of Data with Missing Values
      Tommi Kärkkäinen, Mirka Saarela
      Pages 140-154

  6. Association and Sequential Rule Mining

    1. Front Matter
      Pages 155-155
      PDF
    2. Efficient Mining of High-Utility Sequential Rules
      Souleymane Zida, Philippe Fournier-Viger, Cheng-Wei Wu, Jerry Chun-Wei Lin, Vincent S. Tseng
      Pages 157-171
    3. MOGACAR: A Method for Filtering Interesting Classification Association Rules
      Diana Benavides Prado
      Pages 172-183

  7. Support Vector Machines

    1. Front Matter
      Pages 185-185
      PDF
    2. Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders
      Wanderson Costa, Leila Fonseca, Thales Körting
      Pages 187-198
    3. Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine
      Aruna Govada, Pravin Joshi, Sahil Mittal, Sanjay K. Sahay
      Pages 199-213

  8. Frequent Item Set Mining and Time Series Analysis

    1. Front Matter
      Pages 215-215
      PDF
    2. Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce
      Saber Salah, Reza Akbarinia, Florent Masseglia
      Pages 217-231
    3. Aggregation-Aware Compression of Probabilistic Streaming Time Series
      Reza Akbarinia, Florent Masseglia
      Pages 232-247

  9. Clustering

    1. Front Matter
      Pages 249-249
      PDF
    2. Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)
      Aalaa Mojahed, Joao H. Bettencourt-Silva, Wenjia Wang, Beatriz de la Iglesia
      Pages 251-265
    3. On Bicluster Aggregation and its Benefits for Enumerative Solutions
      Saullo Oliveira, Rosana Veroneze, Fernando J. Von Zuben
      Pages 266-280
    4. Semi-Supervised Stream Clustering Using Labeled Data Points
      Kritsana Treechalong, Thanawin Rakthanmanon, Kitsana Waiyamai
      Pages 281-295
    5. Avalanche: A Hierarchical, Divisive Clustering Algorithm
      Paul K. Amalaman, Christoph F. Eick
      Pages 296-310

  10. Text Mining

    1. Front Matter
      Pages 311-311
      PDF
    2. Author Attribution of Email Messages Using Parse-Tree Features
      Jagadeesh Patchala, Raj Bhatnagar, Sridharan Gopalakrishnan
      Pages 313-327
    3. Query Click and Text Similarity Graph for Query Suggestions
      D. Sejal, K. G. Shailesh, V. Tejaswi, Dinesh Anvekar, K. R. Venugopal, S. S. Iyengar et al.
      Pages 328-341
    4. Offline Writer Identification in Tamil Using Bagged Classification Trees
      Sudarshan Babu
      Pages 342-354

  11. Applications of Data Mining

    1. Front Matter
      Pages 355-355
      PDF
    2. Data Analysis for Courses Registration
      Nada Alzahrani, Rasha Alsulim, Nourah Alaseem, Ghada Badr
      Pages 357-367
    3. Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining
      Darie Moldovan, Simona Mutu
      Pages 368-381
    4. A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization
      Olga Krasotkina, Vadim Mottl
      Pages 382-394
    5. Data Driven Geometry for Learning
      Elizabeth P. Chou
      Pages 395-402
    6. Mining Educational Data to Predict Students’ Academic Performance
      Mona Al-Saleem, Norah Al-Kathiry, Sara Al-Osimi, Ghada Badr
      Pages 403-414
    7. Patient-Specific Modeling of Medical Data
      Guilherme Alberto Sousa Ribeiro, Alexandre Cesar Muniz de Oliveira, Antonio Luiz S. Ferreira, Shyam Visweswaran, Gregory F. Cooper
      Pages 415-424
    8. A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis
      Olga Krasotkina, Vadim Mottl
      Pages 425-437

  12. Data Mining in System Biology, Drug Discovery, and Medicine

    1. Front Matter
      Pages 439-439
      PDF
    2. Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining
      Petra Perner
      Pages 441-451
  13. Back Matter
    Pages 453-454
    PDF

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany, in July 2015.

The 41 full papers presented were carefully reviewed and selected from 123 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.

Keywords

Association rule mining Bayesian network Biclustering Big data Bioinformatics Data cloud geometry Data mining Ensemble method Genetic algorithms Image mining Knowledge discovery in databases Machine learning Ontology Recommendation Robust statistics Semi-supervised learning Social network analysis Support vector machines Uncertainty Visualization

Editors and affiliations

  • Petra Perner
    • 1
  1. 1.IBaILeipzigGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-21024-7
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-21023-0
  • Online ISBN 978-3-319-21024-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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