Advanced Data Mining and Applications [electronic resource] : 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part II / edited by Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang.

Clustering -- Semi-Supervised Clustering Ensemble Evolved by Genetic Algorithm for Web Video Categorization -- A Scalable Approach for General Correlation Clustering -- A fast spectral clustering method based on growing vector quantization for large data sets -- A Novel Deterministic Sampling Technique to Speedup Clustering Algorithms -- Software Clustering using Automated Feature Subset Selection -- The Use of Transfer Algorithm for Clustering Categorical Data -- eDARA: Ensembles DARA -- Efficient mining maximal variant and low usage rate biclusters without candidate maintenance in real function-resource matrix: the DeCluster algorithm -- Association Rule Mining -- MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining -- Pattern Mining -- Mining Frequent Patterns in Print Logs with Semantically Alternative Labels -- Minimising K-Dominating Set in Arbitrary Network Graphs -- Regression -- Logistic Regression Bias Correction for Large Scale Data with Rare Events -- An Automatical Moderating System for FML using Hashing Regression -- Batch-to-Batch Iterative Learning Control Based on Kernel Independent Component Regression Model -- Prediction -- Deep Architecture for Traffic Flow Prediction -- Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction -- Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias -- Protein Interaction Hot Spots Prediction using LS-SVM within the Bayesian Interpretation -- Predicting the survival status of cancer patients with Traditional Chinese Medicine Symptom Variation using Logistic Regression Model -- Feature Extraction -- Exploiting Multiple Features for Learning to Rank in Expert Finding -- Convolution Neural Network for Relation Extraction -- Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis -- kDMI: A Novel Method for Missing Values Imputation Using Two Levels of Horizontal Partitioning in a Data set -- Identification -- Traffic Session Identification based on Statistical Language Model -- Role Identification Based on the Information Dependency Complexity -- Detecting Professional Spam Reviewers -- Chinese Comparative Sentence Identification Based on the Combination of Rules and Statistics -- Privacy Preservation -- Utility Enhancement for Privacy Preserving Health Data Publishing -- Optimizing Placement of Mix Zones to Preserve Users' Privacy for Continuous Query Services in Road Networks -- Applications -- Comparison of Cutoff Strategies for Geometrical Features in Machine Learning-based Scoring Functions -- Bichromatic Reverse Ranking Query in Two Dimensions -- Passive Aggressive Algorithm for Online Portfolio Selection with Piecewise Loss Function -- Mining Item Popularity for Recommender Systems -- Exploring an Ichthyoplankton Database from a Freshwater Reservoir in Legal Amazon -- A Pre-initialization Stage of Population-based Bio-inspired Metaheuristics for Handling Expensive Optimization Problems -- A Hybrid-sorting Semantic Matching Method -- Improving Few Occurrence Feature Performance in Distant Supervision for Relation Extraction -- Cluster Labeling Extraction and Ranking Feature Selection for High Quality XML Pseudo Relevance Feedback Fragments Set -- Informed Weighted Random Projection for Dimension Reduction -- Protocol Specification Inference Based on Keywords Identification -- An Adaptive Collaborative Filtering Algorithm Based on Multiple Features -- Machine Learning -- Ensemble of Unsupervised and Supervised Models with Different Label Spaces -- Cost-sensitive Extreme Learning Machine -- Multi-objective Optimization for Overlapping Community Detection -- Endmember Extraction by Exemplar Finder -- EEG-Based User Authentication in Multilevel Security Systems -- A new fuzzy extreme learning machine for regression problems with outliers or noises.