Note: Title page -- Preface -- Organization -- Table of Contents -- International Workshop on Behavior Informatics (BI 2011) -- Evaluating the Regularity of Human Behavior from Mobile Phone Usage Logs -- Introduction -- Related Work -- Data Preprocessing -- Measures -- Duplication Types -- Accumulated Entropy -- Evaluation of Survey Data -- Conclusion -- References -- Explicit and Implicit User Preferences in Online Dating -- Introduction -- Domain Overview -- User Preferences -- Explicit User Preferences -- Implicit User Preferences -- Are the User Preferences Good Predictors of the Success of User Interactions? -- Explicit User Preferences -- Implicit User Preferences -- Using User Preferences in Recommender Systems -- Hybrid Content-Collaborative Reciprocal Recommender -- Ranking Methods -- Experimental Evaluation -- Results and Discussion -- Conclusions -- References -- Blogger-Link-Topic Model for Blog Mining -- Introduction -- Models for Blog Mining -- Blogger-Link-Topic (BLT) Model -- Blog Classification Framework -- Experiments and Results -- Blogger-Link-Topic Results -- Blog Classification Results -- Results on Co-occurrence of BLT and AT Models -- Conclusion -- References -- A Random Indexing Approach for Web User Clustering and Web Prefetching -- Introduction -- Random Indexing (RI) -- Random Indexing Based Web User Clustering -- Data Preprocessing -- User Modelling Based on Random Indexing -- Single User Pattern Clustering -- Clustering Validity Measures -- Experiments -- Preprocessing of Data Source -- Parameter Setting Investigations -- Common User Profile Creation -- Prefetching for User Groups -- Conclusions -- References -- Emotional Reactions to Real-World Events in Social Networks -- Introduction -- Sentiment Index and Event Indicators -- Sentiment Index for Event Detection -- Event Indicators. , Mood-Based Burst Detection and Bursty Event Extraction -- Bursty Event Detection -- Experimental Results -- Conclusion -- References -- Constructing Personal Knowledge Base: Automatic Key-Phrase Extraction from Multiple-Domain Web Pages -- Introduction -- System Framework -- Preprocessor -- Candidate Phrase Extractor -- Feature Calculation -- Refinement -- Correlation Matrix Generator -- Term Ranking -- Semantic Graph Constructor -- Learning Mechanism -- Evaluation and Experiments -- Datasets and Measures -- Experiment 1: Evaluating Personal Knowledge Base -- Experiment 2: Comparing with KEA -- Conclusions -- References -- Discovering Valuable User Behavior Patterns in Mobile Commerce Environments -- Introduction -- Related Work -- Preliminaries and Definitions -- Proposed Method: UMSPL -- Experimental Evaluations -- Conclusions -- References -- A Novel Method for Community Detection in Complex Network Using New Representation for Communities -- Introduction -- Related Work -- Proposed Method -- Partitioning Vertex -- Degree Entropy -- The Method -- Experiments -- Zachary Karate Club -- Java Compile-Time Dependency -- HEP Literature and Stanford Web Graph -- Conclusion -- References -- Link Prediction on Evolving Data Using Tensor Factorization -- Introduction -- Tensor Decomposition -- Link Prediction -- Evaluation -- Conclusion and Future Work -- References -- Permutation Anonymization: Improving Anatomy for Privacy Preservation in Data Publication -- Introduction -- Motivations -- Preliminaries -- Basic Notations -- Permutation Anonymization -- Preserving Correlation -- Problem Definition -- Generalization Algorithm -- The Partitioning Step -- The Populating Step -- Discussions and Related Work -- Experiments -- Accuracy -- Efficiency -- Conclusion -- References -- Efficient Mining Top-k Regular-Frequent Itemset Using Compressed Tidsets. , Introduction -- Top-k Regular-Frequent Itemsets Mining -- TR-CT: Top-k Regular-Frequent Itemsets Mining Based on Compressed Tidsets -- Compressed Tidset Representation -- Top-k List Structure -- TR-CT Algorithm Description -- An Example -- Performance Evaluation -- Test Environment and Datasets -- Execution Time -- Space Usage -- Conclusion -- References -- A Method of Similarity Measure and Visualization for Long Time Series Using Binary Patterns -- Introduction -- Background and Related Work -- Binary Patterns Based Similarity and Visualization -- Empirical Evaluation -- Hierarchical Clustering -- Visual Effects -- Comparison of Computation Cost -- Conclusions -- References -- A BIRCH-Based Clustering Method for Large Time Series Databases -- Introduction -- Background and Related Work -- Dimensionality Reduction Using Multi-resolution Transforms -- Related Work on Time Series Clustering -- CF Tree and BIRCH Algorithm -- The Proposed Approach - Combination of a Multi-resolution Transform and BIRCH -- How to Determine the Appropriate Scale of a Multi-resolution Transform -- Clustering with k-Means or I-k-Means in Phase 3 -- Experimental Evaluation -- Clustering Quality Evaluation Criteria -- Data Description -- Experimental Results -- Conclusion -- References -- Visualizing Cluster Structures and Their Changes over Time by Two-Step Application of Self-Organizing Maps -- Introduction -- Related Work -- The Proposed Method -- Batch Map -- Two-Step Application of Self-Organizing Maps -- Assigning Angles and Colors to Clusters -- Visualization by Colors and Angles -- Example: Visualization of Clusters in News Articles -- Target Dataset -- Keyword Extraction and Matrix Representation -- Dimension Reduction by Random Projection and LSI -- Final Matrix and Distance Function -- Visualization Results -- Conclusions and Future Work -- References. , Analysis of Cluster Migrations Using Self-Organizing Maps -- Introduction -- Self-Organizing Maps -- Related Work -- Visualizing Migrations -- Transforming 2D Maps to 1D Maps -- Visualizing Migrations -- Analyzing Attribute Interestingness of Migrants -- Application to the WDI datasets -- Conclusion -- Future Work -- References -- Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models Workshop (QIMIE 2011) -- ClasSi: Measuring Ranking Quality in the Presence of Object Classes with Similarity Information -- Introduction and Related Work -- Ranking Quality Measures for Objects in Classes -- Preliminaries: Measuring Ranking Quality -- Class Similarity Ranking Correlation Coefficient ClasSi -- Properties of ClasSi -- ClasSi on Prefixes of Rankings -- Examples -- Conclusion -- References -- The Instance Easiness of Supervised Learning for Cluster Validity -- Introduction -- Instance Easiness -- Generic Definitions -- Instance Easiness for Supervised Learning -- Illustration -- The Clustering-Quality Measure -- Discussion -- Summary -- References -- A New Efficient and Unbiased Approach for Clustering Quality Evaluation -- Introduction -- Unsupervised Recall Precision F-Measure Indexes -- Overall Clustering Quality Estimation -- Cluster Labeling and Content Validation -- Experimentation and Results -- Overall Analysis of the Results -- Quality Indexes Validation -- Conclusion -- References -- A Structure Preserving Flat Data Format Representation for Tree-Structured Data -- Introduction -- Background of the Problem -- Proposed Tree-Structured to Flat Data Conversion -- Experimental Evaluation -- Conclusion and Future Work -- References -- A Fusion of Algorithms in Near Duplicate Document Detection -- Introduction -- Major Algorithms in Duplicate Document Detection. , Shingling, Super Shingling, Mini-wise Independent Permutation Algorithms -- I-Match, Multiple Random Lexicons Based I-Match Algorithms -- Random Projection, Simhash Algorithms -- Model Enhancements -- Shingling Based Simhash Algorithm -- Multiple Random Lexicons Based Simhash Algorithm -- Experiments -- Shingling Based Simhash Algorithm -- Multiple Random Lexicons Based Simhash Algorithm -- Conclusions -- References -- Searching Interesting Association Rules Based on Evolutionary Computation -- Introduction -- Related Work -- Measuring the Similarity -- Measuring the Similarity -- Mining Association Rules by Genetic Network Programming -- Simulations -- Conclusions -- References -- An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases -- Introduction -- Background -- Periodic-Frequent Pattern Model -- Rare Item Problem -- Minimum Constraints Model of Periodic-Frequent Pattern -- Proposed Model -- MaxCPF-Tree: Design, Construction and Mining -- Structure of MaxCPF-Tree -- Constructing MaxCPF-Tree -- Mining of MaxCPF-Tree -- Experimental Results -- Experiment 1 -- Experiment 2 -- Conclusion -- References -- Algorithms to Discover Complete Frequent Episodes in Sequences -- Introduction -- Overview of Frequent Episodes Mining Framework -- Principle for Discovering Minimum Occurrence of Serial Episode -- Algorithms -- An Apriori-Like Algorithm for Serial Episodes -- An FP-Growth-Like Algorithm for Non-overlapped Serial Episodes with Gapmax -- Experiments -- Comparison of Algorithms Ap-epi and Minepi -- Performance of NOE-WinMiner with Gapmax -- Conclusions -- References -- Certainty upon Empirical Distributions -- Introduction -- Contributions -- The Cardinality Scaling of Knowledge -- Uncertainty about Unseen Events -- A Measure of Certainty -- Disjoint Dependent Events -- Entropy Based Measures -- Empirical Validation. , Conclusions.

Note: Front Cover -- Chemical Process Structures and Information Flows -- Copyright Page -- Table of Contents -- Preface -- CHAPTER 1. INTRODUCTION -- 1-1. PHENOMENON-ORIENTED AND SYSTEM-ORIENTED VIEW-POINTS -- 1 -2. THE WHOLE IS MORE THAN THE SUM OF ITS PARTS -- 1-3. OCCURRENCE OF STRUCTURAL PROBLEMS -- 1-4. INFORMATION FLOWS IN PROCESS DESIGN AND ANALYSIS -- 1-5. THE SCOPE OF THIS BOOK -- REFERENCES -- PROBLEMS -- CHAPTER 2. GRAPHS AND DIGRAPHS -- 2-1. A GAME WITH A STRUCTURE -- 2-2. GRAPH-THEORETIC ENTITIES -- 2-3. TREES AND CIRCUITS -- 2-4. OPERATIONS ON GRAPHS -- 2-5. CUTSETS AND CONNECTIVITY -- 2-6. DIRECTED GRAPHS -- 2-7. MATRIX REPRESENTATION OF DIGRAPHS AND GRAPHS -- 2-8. REACHABILITY MATRIX -- 2-9. COMPUTATIONAL CONSIDERATIONS -- NOTATION -- REFERENCES -- PROBLEMS -- CHAPTER 3. PIPELINE NETWORKS -- 3-1. OPTIMAL DESIGN OF PRESSURE RELIEF PIPING NETWORKS -- 3-2. STEADY STATE CONDITIONS IN CYCLIC NETWORKS -- 3-3. ALTERNATIVE PROBLEM FORMULATIONS AND SPECIFICATIONS -- 3-4. INTERACTIVE SYNTHESIS OF DISTRIBUTION NETWORKS -- NOTATION -- REFERENCES -- PROBLEMS -- CHAPTER 4. COMPUTATION SEQUENCE IN PROCESS FLOWSHEET CALCULATIONS -- 4-1. INTRODUCTION -- 4-2. PARTITIONING -- 4-3. OUTPUT ASSIGNMENT -- 4-4. TEARING -- 4-5. COMPUTER PROGRAMS -- NOTATION -- REFERENCES -- PROBLEMS -- CHAPTER 5. SPARSE MATRIX COMPUTATION -- 5-1. INTRODUCTION -- 5-2. SOLUTION OF LINEAR ALGEBRAIC EQUATONS -- 5-3. PIVOTING STRATEGIES -- 5-4. DATA STORAGE AND PROCESSING -- NOTATION -- REFERENCES -- PROBLEMS -- CHAPTER 6. SCHEDULING OF BATCH PLANTS -- 6-1. CHARACTERISTICS OF BATCH PROCESSES -- 6-2. SCHEDULING OF PRODUCTS AND OPERATIONS -- 6-3. SIMPLE MODELS -- 6-4. RECURRENCE RELATIONS AND THE MILP APPROACH -- 6-5. BRANCH AND BOUND METHODS -- 6-6. HEURISTO PROCEDURES -- 6-7. OTHER MODELS OF CHEMICAL ENGINEERING INTEREST -- 6-8. CLOSING REMARKS -- 6-9. COMPUTER PROGRAM -- NOTATION. , REFERENCES -- PROBLEMS -- CHAPTER 7. DESIGN OF BATCH PLANTS -- 7-1. MULTIPRODUCT BATCH PLANTS -- 7-2. MULTIPURPOSE BATCH PLANTS -- 7-3. CLOSING REMARKS -- NOTATION -- REFERENCES -- PROBLEMS -- CHAPTER 8. OBSERVABILITY AND REDUNDANCY -- 8-1. INTRODUCTION -- 8-2. MULTICOMPONENT PROCESS NETWORKS -- 8-3. GENERALIZED PROCESS NETWORKS -- NOTATION -- REFERENCES -- PROBLEMS -- CHAPTER 9. PROCESS DATA RECONCILIATION AND RECTIFICATION -- 9-1. STEADY STATE RECONCILIATON -- 9-2. GROSS ERROR DETECTION AND IDENTIFICATON -- 9-3. CLOSING REMARKS -- NOTATION -- REFERENCES -- Problems -- APPENDIX A: GLOSSARY ON BATCH PROCESSES -- APPENDIX B: ELEMENTS OF PROBABIUTY AND STATISTICS -- CREDITS -- INDEX.

Note: Intro -- Preface -- Organization -- Contents -- Game and Multiagent Systems -- The Application of AlphaZero to Wargaming -- Abstract -- 1 Introduction -- 2 Coral Sea -- 3 AlphaZero -- 4 Differences from Chess and Go -- 4.1 Representations -- 4.2 Asymmetry -- 4.3 Strategic Depth -- 4.4 Hardware -- 5 Experimental Results -- 5.1 AlphaZero with Supervision -- 6 Conclusion -- References -- Helping an Agent Reach a Different Goal by Action Transfer in Reinforcement Learning -- 1 Introduction -- 2 Problem Formulation -- 3 Action Advice in the Different-Goal Situation -- 3.1 Policy-Similar States and Aims of the Proposed Approach -- 3.2 Overview of the Proposed Action Advice Approach -- 3.3 Formulation and Extraction of Decision-Making Information -- 3.4 Learning and Asking Process -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Results and Analysis -- 5 Related Work -- 6 Conclusion -- References -- Predictive Regret-Matching for Cooperating Interceptors to Defeat an Advanced Threat -- 1 Introduction -- 2 Motivation -- 3 Research Problem -- 3.1 Design of the Reachability Calculator Function -- 3.2 Regret-Matching Controller -- 4 Experimental Results and Analysis -- 4.1 Performance of Our Proposed Algorithm -- 4.2 Comparison with the Differential Game-Based Approach -- 5 Conclusion -- References -- Multi-Minimax: A New AI Paradigm for Simultaneously-Played Multi-player Games -- 1 Introduction -- 2 Description of Problem Domain -- 3 Multi-player Game Strategies and Approaches -- 3.1 The Paranoid Algorithm -- 3.2 The Maxn Algorithm -- 3.3 Best Reply Search -- 4 Motivation and Proposed Solution -- 4.1 The Proposed Solution: Multi-Minimax -- 4.2 Added Pruning Method -- 4.3 Implementing the Formalized Method -- 5 Results -- 6 Conclusions -- References. , An Empirical Study of Reward Structures for Actor-Critic Reinforcement Learning in Air Combat Manoeuvring Simulation -- 1 Introduction -- 2 Related Work -- 3 Actor-Critic Reinforcement Learning Background -- 4 Air Combat Simulation -- 5 Experiments and Discussion -- 6 Conclusions and Future Work -- References -- Memory-Based Explainable Reinforcement Learning -- 1 Introduction -- 2 Related Works -- 2.1 Reinforcement Learning -- 2.2 Explainable Artificial Intelligence -- 2.3 Interpretable Reinforcement Learning -- 3 Memory-Based Explainable Reinforcement Learning -- 4 Experimental Set-Up -- 5 Experimental Results -- 5.1 Unbounded Grid World -- 5.2 Bounded Grid World -- 6 Conclusions -- References -- Analysis of Coalition Formation in Cooperative Games Using Crowdsourcing and Machine Learning -- 1 Introduction -- 2 Problem Setting -- 3 Crowdsourcing for Collecting Player Decision-Making Data for Cooperative Games -- 4 Use of Machine Learning to Analyze Player Decision Making in Cooperative Games -- 5 Use of Machine Learning to Predict Division of Profits in Cooperative Games -- 6 Conclusions -- References -- Knowledge Acquisition, Representation, Reasoning -- Exploring Unknown Universes in Probabilistic Relational Models -- 1 Introduction -- 2 Preliminaries -- 2.1 Parameterised Models -- 2.2 Lifted Variable Elimination: An Example -- 3 Models with Unknown Universes -- 3.1 Template Models -- 3.2 Worlds of Constraints -- 3.3 Worlds of Domains -- 3.4 Distribution-Based Semantics -- 4 Query Answering in Unknown Universes -- 5 Conclusion -- References -- Efficient Multiple Query Answering in Switched Probabilistic Relational Models -- 1 Introduction -- 2 Preliminaries -- 2.1 Parameterised Probabilistic Models -- 2.2 Gate Models -- 3 Switched Inference -- 3.1 Parameterised Gate Models -- 3.2 LVE for Query Answering. , 3.3 Switched Lifted Junction Tree Algorithm -- 4 Evaluation -- 5 Conclusion -- References -- Finding ALL Answers to OBDA Queries Using Referring Expressions -- 1 Introduction -- 2 Background and Definitions -- 3 Instance Retrieval over an Unit ABox -- 3.1 The Horn-ALC Case -- 3.2 The EL Case -- 3.3 Finite Representation of Answers -- 4 Extensions -- 5 Summary and Open Problems -- References -- Constructing CP-Nets from Users Past Selection -- 1 Introduction -- 2 Challenge, CP-Net and Bayesian Network -- 2.1 Challenge Model -- 2.2 CP-Net and Bayesian Network (BN) -- 3 Technical Approach -- 3.1 Feature Selection -- 3.2 Layers Extraction -- 3.3 Feature Dependency and Constructing CP-Net -- 3.4 Converter: Probability to Preferences -- 3.5 Layer Binding -- 4 Experimental Evaluation and Results -- 4.1 Data Collection and Gamified System -- 4.2 Evaluation and Model Selection -- 4.3 Evaluation Setting and Results -- 5 Conclusion -- References -- An Efficient Solver for Parametrized Difference Revision -- 1 Introduction -- 2 Preliminaries -- 2.1 Motivation -- 2.2 AGM Belief Revision -- 2.3 Parametrized Difference Operators -- 2.4 All-SAT -- 3 Implementation -- 3.1 Basic Details -- 3.2 Specifying Input -- 4 Belief Revision -- 4.1 Algorithm -- 4.2 Minimization -- 5 Performance -- 5.1 Design Decisions -- 5.2 Experimental Results -- 5.3 Comparison with Related Work -- 6 Conclusion -- References -- Answering Why-Questions Using Probabilistic Logic Programming -- 1 Introduction -- 2 Probabilistic Logic Programming -- 3 The Example Scenario and Our Extension -- 4 System Architecture -- 4.1 Learning Parameters Using SLIPCOVER -- 4.2 Knowledge Base of the QA System -- 4.3 GNL Interface -- 4.4 Meta-interpretive Question Answering -- 5 Evaluation -- 6 Conclusion -- References -- DINE: A Framework for Deep Incomplete Network Embedding -- 1 Introduction -- 2 Related Work. , 2.1 Network Completion -- 2.2 Network Representation Learning -- 3 Preliminary -- 3.1 Notations -- 3.2 Problem Formulation -- 4 Design of DINE -- 4.1 Recovery of Incomplete Network -- 4.2 Recovered Network Embedding MVC-DNER -- 5 Experiments -- 5.1 Datasets -- 5.2 Baseline Methods -- 5.3 Parameter Settings -- 5.4 Experimental Results -- 6 Conclusion -- References -- Predictive Representation Learning in Motif-Based Graph Networks -- 1 Introduction -- 2 Predictive Representation Learning -- 2.1 Problem Definition -- 2.2 Existent Edge Representations -- 2.3 Nonexistent Edge Representations -- 2.4 Joint Learning -- 3 Experiments -- 3.1 Data Sets -- 3.2 Baseline Methods -- 3.3 Experimental Settings -- 3.4 Experimental Results -- 4 Conclusion -- References -- Machine Learning and Applications -- Online K-Means Clustering with Lightweight Coresets -- 1 Introduction -- 2 Preliminaries -- 2.1 Data Stream Model -- 2.2 K-Means++ -- 2.3 Online K-Means -- 2.4 Lightweight Coresets -- 2.5 Very Large Datasets -- 3 Related Work -- 4 Proposed Method -- 5 Experiments -- 5.1 Evaluation Criteria -- 5.2 Datasets -- 5.3 Experimental Setup -- 5.4 Synthetic Datasets -- 5.5 Real Datasets -- 6 Conclusion -- References -- Solving Safety Problems with Ensemble Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 The Reinforcement Learning Framework -- 3.2 Ontology and SUMO -- 4 RL Ensemble Framework -- 5 Experiments in the Safety Gridworlds -- 5.1 An Ensemble Agent for Safety Domains -- 5.2 Experiment Configuration -- 6 Results -- 6.1 Self Modification Gridworld -- 6.2 Safe Exploration -- 6.3 Distributional Shift -- 6.4 Discussion -- 7 Conclusion -- References -- Sharpening the BLADE: Missing Data Imputation Using Supervised Machine Learning -- 1 Introduction -- 2 Related Work -- 3 The BLADE Dataset and Missing Values -- 4 Methodology. , 4.1 Performance Metrics -- 5 Experimental Evaluation -- 5.1 Algorithm Comparisons for Turnover -- 5.2 Impact of Input Features -- 5.3 Impact of Time Spans -- 5.4 Experimental Results for FTE -- 5.5 Processing Time -- 6 Discussion -- 7 Conclusion -- References -- Predicting Financial Well-Being Using Observable Features and Gradient Boosting -- 1 Introduction -- 2 Background, Related Work and Motivation -- 3 Dataset -- 3.1 Pre-processing -- 4 Methods -- 4.1 Model Development and Testing -- 5 Results -- 5.1 Machine Learning Model Results -- 5.2 Exploratory Factory Analyses -- 6 Discussion -- 6.1 Predictability -- 6.2 Important Features -- 6.3 Limitations and Future Work -- 7 Conclusion -- References -- Fast Filtering for Nearest Neighbor Search by Sketch Enumeration Without Using Matching -- 1 Introduction -- 2 Preliminaries -- 2.1 Nearest Neighbor Search Using Sketches -- 2.2 Sketches Based on Ball Partitioning -- 2.3 Fast Filtering by Sketch Enumeration -- 3 Fast Search Using Sketch Enumeration in score1 Order -- 4 Experiments -- 4.1 The Enumeration of Sketches in score1 Order -- 4.2 The Optimal Sketch Width for Database 1 -- 4.3 The Effects by Data Dimension and Database Size -- 5 Concluding Remarks -- References -- Evaluating the Boundaries of Big Data Environments for Machine Learning -- 1 Introduction -- 2 Background and Motivation -- 3 Methodology -- 3.1 Research Setting -- 4 Results -- 4.1 Large Data Set Analysis -- 4.2 Small Data Set Analysis -- 4.3 Concurrent Query Analysis -- 5 Discussion -- 6 Conclusion -- References -- Sequence-to-Sequence Imputation of Missing Sensor Data -- 1 Introduction and Related Work -- 2 Related Work -- 3 Model -- 3.1 Architecture -- 3.2 Scaling Factors -- 3.3 Backpropagation with Scaling Factors -- 3.4 Output Layer -- 4 Experiments -- 5 Results and Discussion -- 6 Conclusion -- References. , The Futility of Bias-Free Learning and Search.

Note: Title Page -- Preface -- Organization -- Table of Contents - Part I -- Supervised Learning: Active, Ensemble, Rare-Class and Online -- Time-Evolving Relational Classification and Ensemble Methods -- Introduction -- Related Work -- Temporal-Relational Classification Framework -- Temporal Granularity -- Temporal Influence: Links, Attributes, Nodes -- Temporal-Relational Classifiers -- Temporal Ensemble Methods -- Methodology -- Datasets -- Temporal Models -- Empirical Results -- Single Models -- Temporal-Ensemble Models -- Conclusion -- References -- Active Learning for Hierarchical Text Classification -- Introduction -- A Novel Multi-oracle Setting -- A New Framework of Hierarchical Active Learning -- Unlabeled Pool Building Policy -- Leveraging Oracle Answers -- Experimental Configuration -- Datasets -- Performance Measure -- Active Learning Setup -- Empirical Study -- Standard Hierarchical Active Learner -- Leveraging Positive Examples in Hierarchy -- Leveraging Negative Examples in Hierarchy -- Conclusion -- References -- TeamSkill Evolved: Mixed Classification Schemes for Team-Based Multi-player Games -- Introduction -- Related Work -- Proposed Approaches -- TeamSkill-AllK-Ev-OL1 -- TeamSkill-AllK-Ev-OL2 -- TeamSkill-AllK-Ev-OL3 -- Using Game-Specific Data during Classification -- TeamSkill-AllK-EVGen -- TeamSkill-AllK-EVMixed -- Evaluation -- Dataset -- Overall Results -- Results over Time -- Online Classification Variants -- Discussion -- Conclusions -- References -- A Novel Weighted Ensemble Technique for Time Series Forecasting -- Introduction -- Forecasts Combination Methods -- The Proposed Ensemble Technique -- Mathematical Description -- Optimization of the Combination Weights -- Approach for Weights Determination -- Three Time Series Forecasting Models -- Autoregressive Integrated Moving Average (ARIMA). , Artificial Neural Networks (ANNs) -- Elman Artificial Neural Networks (EANNs) -- Experiments and Discussions -- Conclusions -- References -- Techniques for Efficient Learning without Search -- Introduction -- The AnDE Family of Algorithms -- AODE -- AnDE -- Optimising Memory Consumption -- Optimising Testing Time -- Evaluation -- Test Environment -- Optimised Memory Consumption -- Optimised Testing -- The Evaluation of A3DE -- A3DE Performance on Large Datasets -- Conclusions -- References -- An Aggressive Margin-Based Algorithm for Incremental Learning -- Introduction -- Online Passive-Aggressive Algorithm -- Incremental Passive-Aggressive Learning Algorithm -- Experiments -- Conclusion -- References -- Two-View Online Learning -- Introduction -- Related Work -- Two-View Online Passive Aggressive Learning -- Problem Setting -- Relationship between Views -- Two-View Passive Aggressive Algorithm -- Performance Evaluation -- View Difference Comparison -- Ads Dataset -- Product Review Dataset -- WebKB Course Dataset -- Conclusion and Open Problems -- References -- A Generic Classifier-Ensemble Approach for Biomedical Named Entity Recognition -- Introduction -- The Generic Genetic Classifier-Ensemble Approach -- Feature Set and SVM Based Classifier -- Generic Genetic Classifier-Ensemble Algorithm -- Experiments and Results -- Conclusion and Future Work -- References -- Neighborhood Random Classification -- Introduction -- Basic Concepts -- Notations -- Neighborhood Structure -- Neighborhood Classifiers -- Partition by Neighborhood Graphs -- Ensemble Method Classifier Based on Neighborhood -- Sampling Procedures -- Aggregating Function -- Evaluation -- Implementation of RNC -- Other Methods -- The Test -- Computational Analysis -- Conclusion and Further Work -- References. , SRF: A Framework for the Study of Classifier Behavior under Training Set Mislabeling Noise -- Introduction -- Background and Related Work -- The Sigmoid Rule Framework -- Sigmoid Rule Framework (SRF) Dimensions -- Comparing Algorithms -- Experimental Evaluation -- Using SRF -- Statistical Analysis -- Conclusions -- References -- Building Decision Trees for the Multi-class Imbalance Problem -- Introduction -- Methods -- Decomposition Techniques -- Decision Trees -- Analysis of the Splitting Criteria -- Experiments -- Configuration -- Statistical Tests -- Results -- Related Work -- Conclusion and Discussion -- References -- Scalable Random Forests for Massive Data -- Introduction -- Related Work -- Scalable Random Forest Algorithm -- Breadth-First Random Forest Construction -- Scalable Random Forest Algorithm -- Mapper, Reducer and Controller -- Experiments -- Data Sets -- Experiment Settings -- Performance Results -- Scalability -- Conclusions -- References -- Hybrid Random Forests: Advantages of Mixed Trees in Classifying Text Data -- Introduction -- Hybrid Random Forests -- Framework for Building Hybrid Random Forest -- Decision Tree Algorithms -- Algorithm -- Evaluation Methods -- Experiments -- Datasets -- Test Accuracy Improvement -- Performance Comparisons of other Text Classification Method -- Conclusion and Future Work -- References -- Learning Tree Structure of Label Dependency for Multi-label Learning -- Introduction -- Related Work -- The Concept of Multi-label Learning -- Learning a Tree Structure of Labels -- Experiment Design and Analysis -- The Description of Datasets -- Evaluation Criteria -- Algorithms and Settings -- Experimental Results and Analysis -- Conclusion -- References -- Multiple Instance Learning for Group Record Linkage -- Introduction -- Related Work -- Group Linkage Using Multiple Instance Learning. , Instance Selection and Classifier Learning -- Instance Classification -- Group Record Linkage -- Experiments and Evaluation -- Synthetic Data Results -- Historical Census Data Results -- Conclusion -- References -- Incremental Set Recommendation Based on Class Differences -- Introduction -- Definition -- Set Recommendation Based on Class Differences -- Example -- ZDD and VSOP -- Set Recommendation with ZDD Structure -- Experiments -- Performance Evaluation -- Example : Internet Shopping Advertising -- Example : AOL Search Logs -- Summary and Future Works -- References -- Active Learning for Cross Language Text Categorization -- Introduction -- Related Work -- Active Learning for CLTC -- Cross Language Text Categorization -- Apply Active Learning to CLTC -- Double Viewed Active Learning -- Two Views of the Problem -- Double Viewed Active Learning -- Evaluation -- Experimental Setup -- Results and Discussions -- Conclusions and Future Works -- References -- Evasion Attack of Multi-class Linear Classifiers -- Introduction -- Problem Setup -- Multi-class Linear Classifier -- Attack of Adversary -- Adversarial Cost -- Disguised Instances -- Theory of Evasion Attack -- Algorithm for Approximating -IMAC -- Experiments -- Spam Disguising -- Face Camouflage -- Conclusions -- References -- Foundation of Mining Class-Imbalanced Data -- Introduction -- Upper Bounds -- Error Rate on a Particular Class -- Cost-Weighted Error -- Empirical Results with Specific Learner -- Datasets and Settings -- Experimental Design and Results -- Conclusions -- References -- Active Learning with c-Certainty -- Introduction -- Previous Works -- c-Certainty Labeling -- BMO (Best-Multiple-Oracle) with c-Certainty -- Selecting the Best Oracle -- Active Learning Process of BMO -- Experiments -- Results on Faithful Oracles -- Results on Unfaithful Oracles -- Conclusion -- References. , A Term Association Translation Model for Naive Bayes Text Classification -- Introduction -- Related Work -- Terminology -- Naive Bayes Classifier -- Language Models for Information Retrieval -- The Term Association Translation Models -- Language Models for Text Classification -- Translation Model Estimation Using Joint Probability Model -- Translation Model Estimation Based on Mutual Information -- Experiments -- Corpora -- Performance Measure -- Experimental Results -- Conclusion and Future Work -- References -- A Double-Ensemble Approach for Classifying Skewed Data Streams -- Introduction -- Background and Motivations -- Performance Metrics -- Classification Methods for Skewed Data -- Classification Methods for Streaming Data -- Motivations -- Proposed Method -- Framework of the Method -- Multi-objective Optimization -- Reliability Estimation -- Experimental Evaluation -- Datasets -- Experimental Protocol -- Results -- Conclusions -- References -- Generating Balanced Classifier-Independent Training Samples from Unlabeled Data -- Introduction -- Related Work -- Generating Balanced Training Data -- Overview -- Semi-supervised Clustering -- Determine the Optimal Number to Samples from Each Cluster -- Leveraging Domain Knowledge -- Maximum Entropy Sampling -- Experiments and Evaluation -- Evaluation Setup -- Comparison of Class Distribution in Training Samples -- Comparison of Classification Performance -- Impact of Domain Knowledge -- Conclusion -- References -- Nyström Approximate Model Selection for LSSVM -- Introduction -- Least Squares Support Vector Machine -- Approximating LSSVM Using Nyström Method -- Error Analysis -- Approximate Model Selection for LSSVM -- Experiments -- Experimental Scheme -- Effectiveness -- Conclusion -- References -- Exploiting Label Dependency for Hierarchical Multi-label Classification -- Introduction. , Our Contributions.

Note: Title -- Preface -- Organization -- Table of Contents -- Pattern Mining: Networks, Graphs, Time-Series and Outlier Detection -- Heterogeneous Ensemble for Feature Drifts in Data Streams -- Introduction -- Related Work -- Proposed Framework -- Feature Selection Block -- Ensemble Block -- Experiments and Analysis -- Experimental Setup -- Experimental Results -- Conclusions -- References -- OMC-IDS: At the Cross-Roads of OLAP Mining and Intrusion Detection -- Introduction -- Scrutiny of the Related Work -- OMC-IDS: Intrusion Detection Based on Olap Mining and Classification -- Audit Data Cube: Construction and Manipulation -- Multidimensional Association Rule Mining -- Classification -- Experimental Results -- Conclusion and Perspectives -- References -- Towards Linear Time Overlapping Community Detection in Social Networks -- Introduction -- Related Work -- SLPA: Speaker-Listener Label Propagation Algorithm -- Tests in Synthetic Networks -- Methodology -- Identifying Overlapping Communities in LFR -- Identifying Overlapping Nodes in LFR -- Tests in Real-World Social Networks -- Identifying Overlapping Communities in Social Networks -- Identifying Overlapping Communities in Bipartite Networks -- Identifying Overlapping Nested Communities -- Conclusions -- References -- WeightTransmitter:Weighted Association Rule Mining Using LandmarkWeights -- Introduction -- Related Work -- Problem Definition -- Weight Transmitter Model -- Experimental Results -- Datasets -- Weight Estimation Evaluation -- Rule Evaluation -- Runtime Evaluation -- Conclusions -- References -- Co-occurring Cluster Mining for Damage Patterns Analysis of a Fuel Cell -- Introduction -- The Proposed Method: Co-occurring Cluster Mining -- Problems of the Conventional Methods -- The Requirements of a Co-occurrence Pattern -- The Objective Function -- The Algorithm -- Application to AE Data. , Damage Evaluation Test of Fuel Cells -- Division into Basket -- Calculation of Distance between AE Events -- The Design of the Object Function -- The Results of Extracted Damage Patterns -- Conclusion -- References -- New Exact Concise Representation of Rare Correlated Patterns: Application to Intrusion Detection -- Introduction and Motivations -- Basic Notions -- Characterization of the Rare Correlated Patterns -- Definition and Properties -- Characterization of the Rare Correlated Equivalence Classes -- The RcprMiner Algorithm -- Experimental Results -- Application to Intrusion Detection -- Description of the KDD 99 Dataset -- Summary of Experimentations and Discussion of Obtained Results -- Conclusion and Future Works -- References -- Life Activity Modeling of News Event on Twitter Using Energy Function -- Introduction -- Related Work -- Modeling Life Activity Using Energy Function -- Definition of Energy Function -- Energy of A Single Tweet -- Constant Growth and Decay -- Single-Pass Clustering with Energy Function -- Experiments and Evaluation -- Data Preparation -- Training Energy Transferred Factor and Decayed Factor -- News Event Detection Comparisons -- Conclusions -- References -- Quantifying Reciprocity in LargeWeighted Communication Networks -- Introduction -- Related Work -- Data Description -- Proposed Model: 3PL -- Comparison of 3PL to Competing Models -- Goodness of Fit -- 3PL at Work -- Reciprocity and Local Network Topology -- Weighted Reciprocity Metrics -- Reciprocity and Network Overlap -- Reciprocity and Degree Similarity -- Conclusions -- References -- Hierarchical Graph Summarization: Leveraging Hybrid Information through Visible and Invisible Linkage -- Introduction -- Related Work -- Basic Graph Summarization -- Hierarchical Graph Summarization -- Overview -- Incorporating Hierarchical Linkage. , Estimation of Document/Cluster Importance -- Experiments and Evaluation -- Dataset -- Evaluation Metrics -- Algorithms for Comparison -- Overall Performance Comparison -- Parameter Tuning -- Conclusions -- References -- Mining Mobile Users' Activities Based on Search Query Text and Context -- Introduction -- Related Work -- Methodology -- Data and Preprocessing -- Text and Context-Based User Activity Model -- Inference of Model -- Constrained TCUAM Model -- Experiment -- Data Set -- Experimental Setup -- Evaluation -- Results -- Case Study -- Conclusion -- References -- Spread of Information in a Social Network Using Influential Nodes -- Introduction -- Motivation -- Literature Review -- Maximizing Influence Spread -- Problem Definition -- Our Approach -- Detecting the core -- Experimental Results -- Conclusion -- References -- Discovering Coverage Patterns for Banner Advertisement Placement -- Introduction -- Model of Coverage Patterns -- Coverage Patterns -- Mining Coverage Patterns -- Coverage Pattern Extraction Algorithm -- Experimental Results -- Coverage Pattern Generation -- Scalability Experiment -- Usefulness of Coverage Patterns -- Conclusions and Future Work -- References -- Discovering Unknown But Interesting Items on Personal Social Network -- Introduction -- Related Works -- Social Networking -- Recommendation Systems -- Unknown But Interesting Recommendation System -- System Architecture -- Unknown But Interesting Algorithm -- Experiments -- Methodology -- Performance Evaluation -- Conclusions -- References -- The Pattern Next Door: Towards Spatio-sequential Pattern Discovery -- Introduction -- Related Work -- Spatio-sequential Patterns: Concepts and Definitions -- Preliminaries -- Spatio-sequential Patterns -- Spatio-temporal Participation -- Extraction of Spatio-sequential Patterns -- Experiments -- Conclusion and Perspectives. , References -- Accelerating Outlier Detection with Uncertain Data Using Graphics Processors -- Introduction -- Related Work -- Algorithm for Outlier Detection with Uncertain Data -- Serial and Parallel Implementations -- Serial Methods -- Parallel Methods -- Experimental Results -- Performance -- Quality -- Conclusion -- References -- Finding Collections of k-Clique Percolated Components in Attributed Graphs -- Introduction -- Pattern Definition -- Mining CoHoP Patterns -- Experiments -- Illustration of the Interest of the Patterns -- Performance Study -- Related Work -- Conclusion -- References -- Reciprocal and Heterogeneous Link Prediction in Social Networks -- Introduction -- Related Work -- Problem Statement -- Methods -- Feature Construction -- Learning and Testing -- Experiments -- Setup -- Results -- Conclusion -- References -- Detecting Multiple Stochastic Network Motifs in Network Data -- Introduction -- Network Motif Analysis in Social Media -- Canonical Forms of Subgraphs for Modeling Stochastic Motifs -- Finite Mixture Model -- Basic EM Algorithm -- Learning the Optimal Number of Motifs -- Experimental Results -- Results on Synthetic Networks -- Results on Benchmark Datasets -- Effectiveness of CEM2 in Estimating Optimal Number of Motifs -- Computational Complexity -- Conclusion and Future Works -- References -- Scalable Similarity Matching in Streaming Time Series -- Introduction and Motivations -- Key Notions -- Related Work -- The TriCons Algorithm -- Main Notions of the TriCons Algorithm -- Description of the TriCons Algorithm -- Experimental Results -- Conclusion and Future Work -- References -- Scalable Mining of Frequent Tri-concepts from Folksonomies -- Introduction and Motivations -- KeyNotions -- Related Work -- TheTRICONS Algorithm -- Main Notions of the TRICONS Algorithm -- Description of the TRICONS Algorithm. , Experimental Results -- Conclusion and Future Work -- References -- SHARD: A Framework for Sequential, Hierarchical Anomaly Ranking and Detection -- Introduction -- Related Literature -- Hierarchical Anomalies -- Anomaly Detection Framework -- Ontology Template -- Anomaly Tree Structure -- Baseline Anomaly Detectors -- Ranking Anomalies -- Anomaly Tree Visualization -- Empirical Evaluation -- Synthetic Data Experiments -- Event Attendance Data Results -- Climatology Data Results -- Stock Data Results -- Discussion -- Conclusions and Future Work -- References -- Instant Social Graph Search -- Introduction -- Problem Definition -- Algorithms -- Basic Ideas -- The Path Algorithm -- The Influence Algorithm -- The Diversity Algorithm -- Experimental Results -- Experiment Setup -- Accuracy Performance -- Analysis and Discussions -- Related Work -- Conclusions -- References -- Data Manipulation: Pre-processing and Dimension Reduction -- Peer Matrix Alignment: A New Algorithm -- Introduction -- Problem Definition -- The Proposed Algorithm -- Examples -- Experiments -- Related Works -- Conclusions -- References -- Domain Transfer Dimensionality Reduction via Discriminant Kernel Learning -- Introduction -- Brief Review of Prior Work -- Discriminant Multiple Kernel Learning -- Transfer Learning and Maximum Mean Discrepancy Formulation -- Semi-supervised Discriminant Analysis in Cross-Domain -- Standard Discriminant Kernel Learning Analysis -- Domain Transfer Kernel Learning for Discriminant Analysis -- Experiment -- Data Sets and Experiment Setup -- Experimental Results -- Conclusion -- References -- Prioritizing Disease Genes by Bi-Random Walk -- Introduction -- Methods -- Loss Function -- Bi-Random Walk -- Unbalanced Bi-Random Walk -- BiRW Algorithms -- Comparison of Random Walk Algorithms -- Experiments and Discussions -- Data Preparation. , Comparison with Other Methods.