Machine Learning and Data Mining in Pattern Recognition

1. Front Matter

   Pages I-IX

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2. Graph Mining

   1. Front Matter

      Pages 1-1

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   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

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   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

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   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

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   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

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   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

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   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

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.

• 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

• IBaI, Leipzig, Germany

  Petra Perner

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