Note: Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- About the Authors -- Abbreviations -- Part IIntroduction -- 1 Overview and Contributions -- 1.1 Introduction -- 1.2 Research Issues on Unsupervised Outlier Detection -- 1.3 Overview of the Book -- 1.4 Contributions -- 1.5 Conclusions -- 2 Developments in Unsupervised Outlier Detection Research -- 2.1 Introduction -- 2.1.1 A Brief Overview of the Early Developments in Outlier Analysis -- 2.2 Some Standard Unsupervised Outlier Detection Approaches -- 2.2.1 Probabilistic Model-Based Outlier Detection Approach -- 2.2.2 Clustering-Based Outlier Detection Approaches -- 2.2.3 Distance-Based Outlier Detection Approaches -- 2.2.4 Density-Based Outlier Detection Approaches -- 2.2.5 Outlier Detection for Time Series -- 2.3 Performance Evaluation Metrics of Outlier Detection Approaches -- 2.3.1 Precision, Recall and Rank Power -- 2.4 Conclusions -- References -- Part IINew Developments in Unsupervised Outlier Detection Research -- 3 A Fast Distance-Based Outlier Detection Technique Using a Divisive Hierarchical Clustering Algorithm -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 Distance-Based Outlier Detection Research -- 3.2.2 A Divisive Hierarchical Clustering Algorithm for Approximate kNN Search -- 3.2.3 An Efficiency Analysis of DHCA for Distance-Based Outlier Detection -- 3.3 The Proposed Fast Distance-Based Outlier Detection Algorithm -- 3.3.1 A Simple Idea -- 3.3.2 The Proposed CPU-Efficient DB-Outlier Detection Method -- 3.3.3 Time Complexity Analysis -- 3.3.4 Data Structure for Implementing DHCA -- 3.4 Scale to Very Large Databases with I/O Efficiency -- 3.5 Performance Evaluation -- 3.5.1 Data Characteristics -- 3.5.2 The Impact of Input K on Running Time -- 3.5.3 Comparison with Other Methods -- 3.5.4 Effectiveness of DHCA for kNN Search. , 3.5.5 The Impact of Curse of Dimensionality -- 3.5.6 Scale to Very Large Databases with I/O Efficiency -- 3.5.7 Discussion -- 3.6 Conclusions -- References -- 4 A k-Nearest Neighbor Centroid-Based Outlier Detection Method -- 4.1 Introduction -- 4.2 K-means Clustering and Its Application to Outlier Detection -- 4.2.1 K-means Clustering -- 4.2.2 K-means Clustering-Based Outlier Detection -- 4.3 A kNN-Centroid-Based Outlier Detection Algorithm -- 4.3.1 General Idea -- 4.3.2 Definition for an Outlier Indicator -- 4.3.3 Formal Definition of kNN-Based Centroid -- 4.3.4 Two New Formulations of Outlier Factors -- 4.3.5 Determination of k -- 4.3.6 The Complexity Analysis -- 4.3.7 The Proposed Outlier Detection Algorithm -- 4.4 A Performance Study -- 4.4.1 Performance on Synthetic Datasets -- 4.4.2 Performance on Real Datasets -- 4.4.3 Performance on High-Dimensional Real Datasets -- 4.4.4 Discussion -- 4.5 Conclusions -- References -- 5 A Minimum Spanning Tree Clustering-Inspired Outlier Detection Technique -- 5.1 Introduction -- 5.2 Background -- 5.2.1 Minimum Spanning Tree-Based Clustering -- 5.2.2 Minimum Spanning Tree Clustering-Based Outlier Detection -- 5.3 An Improved MST-Clustering-Inspired Outlier Detection Algorithm -- 5.3.1 A Simple Idea -- 5.3.2 Two New Outlier Factors -- 5.3.3 The Proposed MST-Clustering-Inspired Outlier Detection Algorithm -- 5.3.4 Time Complexity Analysis -- 5.4 A Performance Study -- 5.4.1 Performance on Synthetic Datasets -- 5.4.2 Performance on Multi-dimensional Real Datasets -- 5.4.3 Performance of the Proposed Algorithm with Varying SOM-TH -- 5.5 Concluding Remarks -- References -- 6 A k-Nearest Neighbour Spectral Clustering-Based Outlier Detection Technique -- 6.1 Introduction -- 6.2 Spectral Clustering and Its Application to Outlier Detection -- 6.2.1 Preliminaries -- 6.2.2 Spectral Clustering-Based Outlier Detection. , 6.3 The Proposed Spectral Clustering-Based Outlier Mining Algorithm -- 6.3.1 A Simple Idea -- 6.3.2 The Proposed Outlier Detection Algorithm -- 6.3.3 Complexity Analysis -- 6.4 Experimental Results -- 6.4.1 Performance of Our Algorithm on Synthetic Data -- 6.4.2 Performance of Our Algorithm on Real Data -- 6.5 Discussion -- 6.6 Conclusions -- References -- 7 Enhancing Outlier Detection by Filtering Out Core Points and Border Points -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Density-Based Clustering with DBSCAN -- 7.2.2 Density-Based Clustering for Outlier Detection -- 7.3 The Proposed Enhancer for Outlier Mining -- 7.3.1 A Simple Idea -- 7.3.2 Some Definitions -- 7.3.3 Our Proposed Outlier Detection Algorithm -- 7.3.4 The Complexity Analysis -- 7.4 Experiments and Results -- 7.4.1 Performance of Our Algorithm on Synthetic Data -- 7.4.2 Performance of Our Algorithm on Real Data -- 7.5 Conclusions -- References -- Part IIIApplications -- 8 An Effective Boundary Point Detection Algorithm Via k-Nearest Neighbors-Based Centroid -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Outlier and Boundary Point Detection -- 8.2.2 EMST Algorithms -- 8.3 Boundary Point Detection Based on kNN Centroid -- 8.3.1 Definitions -- 8.3.2 The Proposed Boundary Point and Outlier Detection Algorithm -- 8.3.3 The Complexity Analysis -- 8.4 The Proposed Fast Approximate EMST Algorithm -- 8.4.1 Our Clustering-Inspired EMST Algorithm -- 8.4.2 Time Complexity Analysis -- 8.5 Experiments and Results -- 8.5.1 Performance Evaluation of the Proposed Boundary Point Detection Algorithm -- 8.5.2 Performance Evaluation of the Fast Approximate EMST Algorithm -- 8.6 Conclusions -- References -- 9 A Nearest Neighbor Classifier-Based Automated On-Line Novel Visual Percept Detection Method -- 9.1 Introduction -- 9.2 A Percept Learning System -- 9.2.1 Feature Generation. , 9.2.2 Similarity Measure -- 9.2.3 Percept Formation -- 9.2.4 A Fast Approximate Nearest Neighbor Classifier -- 9.3 An On-Line Novelty Detection Method -- 9.3.1 A Threshold Selection Method -- 9.3.2 Eight-Connected Structure Element Filter -- 9.3.3 Tree Update Method -- 9.4 Experiments and Results -- 9.4.1 Experiment I: An Indoor Environment -- 9.4.2 Experiment II: An Outdoor Environment -- 9.5 Conclusions -- References -- 10 Unsupervised Fraud Detection in Environmental Time Series Data -- 10.1 Introduction -- 10.2 Related Work -- 10.2.1 Point Outliers -- 10.2.2 Shape Outliers -- 10.3 Method -- 10.3.1 A Simple Idea -- 10.3.2 Selecting an Appropriate Threshold for Fraud Detection -- 10.3.3 The Complexity Analysis -- 10.4 Experiments and Results -- 10.4.1 Fraud Detection on Wastewater Discharge Concentration Data -- 10.4.2 Fraud Detection on Gas Emission Concentration Data -- 10.5 Conclusions -- References.