Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings (Lecture Notes in Computer Science)

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.

Table of Contents

Part 1 Graph Mining
Chapter 1 Greedy Graph Edit Distance
Chapter 2 Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation
Chapter 3 Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning

Part 2 Classification and Regression
Chapter 4 Local and Global Genetic Fuzzy Pattern Classifiers
Chapter 5 IKLTSA: An Incremental Kernel LTSA Method

Part 3 Sentiment Analysis
Chapter 6 SentiSAIL: Sentiment Analysis in English, German and Russian
Chapter 7 Sentiment Analysis for Government: An Optimized Approach

Part 4 Data Preparation and Missing Values
Chapter 8 A Novel Algorithm for the Integration of the Imputation of Missing Values and Clustering
Chapter 9 Improving the Algorithm for Mapping of OWL to Relational Database Schema
Chapter 10 Robust Principal Component Analysis of Data with Missing Values

Part 5 Association and Sequential Rule Mining
Chapter 11 Efficient Mining of High-Utility Sequential Rules
Chapter 12 MOGACAR: A Method for Filtering Interesting Classification Association Rules

Part 6 Support Vector Machines
Chapter 13 Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders
Chapter 14 Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine

Part 7 Frequent Item Set Mining and Time Series Analysis
Chapter 15 Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce
Chapter 16 Aggregation-Aware Compression of Probabilistic Streaming Time Series

Part 8 Clustering
Chapter 17 Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)
Chapter 18 On Bicluster Aggregation and its Benefits for Enumerative Solutions
Chapter 19 Semi-Supervised Stream Clustering Using Labeled Data Points
Chapter 20 Avalanche: A Hierarchical, Divisive Clustering Algorithm

Part 9 Text Mining
Chapter 21 Author Attribution of Email Messages Using Parse-Tree Features
Chapter 22 Query Click and Text Similarity Graph for Query Suggestions
Chapter 23 Offline Writer Identification in Tamil Using Bagged Classification Trees

Part 10 Applications of Data Mining
Chapter 24 Data Analysis for Courses Registration
Chapter 25 Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining
Chapter 26 A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization
Chapter 27 Data Driven Geometry for Learning
Chapter 28 Mining Educational Data to Predict Students’ Academic Performance
Chapter 29 Patient-Specific Modeling of Medical Data
Chapter 30 A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis

Part 11 Data Mining in System Biology, Drug Discovery, and Medicine
Chapter 31 Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining