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Machine Learning and Knowledge Discovery in Databases

Overview of attention for book
Machine Learning and Knowledge Discovery in Databases
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups
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    Chapter 2 Maximal Closed Set and Half-Space Separations in Finite Closure Systems
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    Chapter 3 Sets of Robust Rules, and How to Find Them
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    Chapter 4 A Framework for Deep Constrained Clustering - Algorithms and Advances
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    Chapter 5 A Framework for Parallelizing Hierarchical Clustering Methods
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    Chapter 6 Unsupervised and Active Learning Using Maximin-Based Anomaly Detection
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    Chapter 7 The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection
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    Chapter 8 Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations
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    Chapter 9 Uncovering Hidden Block Structure for Clustering
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    Chapter 10 CatchCore : Catching Hierarchical Dense Subtensor
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    Chapter 11 Fast and Parallelizable Ranking with Outliers from Pairwise Comparisons
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    Chapter 12 Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
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    Chapter 13 Robust Anomaly Detection in Images Using Adversarial Autoencoders
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    Chapter 14 Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithms
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    Chapter 15 Pattern-Based Anomaly Detection in Mixed-Type Time Series
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    Chapter 16 k Is the Magic Number—Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
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    Chapter 17 From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder
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    Chapter 18 Joint Multi-source Reduction
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    Chapter 19 Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold
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    Chapter 20 On the Stability of Feature Selection in the Presence of Feature Correlations
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    Chapter 21 Agnostic Feature Selection
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    Chapter 22 User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
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    Chapter 23 Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries
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    Chapter 24 Node Representation Learning for Directed Graphs
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    Chapter 25 Link Prediction via Higher-Order Motif Features
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    Chapter 26 SoRecGAT: Leveraging Graph Attention Mechanism for Top-N Social Recommendation
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    Chapter 27 Graph Signal Processing for Directed Graphs Based on the Hermitian Laplacian
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    Chapter 28 Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification
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    Chapter 29 node2bits : Compact Time- and Attribute-Aware Node Representations for User Stitching
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    Chapter 30 A Soft Affiliation Graph Model for Scalable Overlapping Community Detection
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    Chapter 31 Node Classification for Signed Social Networks Using Diffuse Interface Methods
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    Chapter 32 Triangle Completion Time Prediction Using Time-Conserving Embedding
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    Chapter 33 Adjustment Criteria for Recovering Causal Effects from Missing Data
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    Chapter 34 An Algorithm for Reducing the Number of Distinct Branching Conditions in a Decision Forest
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    Chapter 35 Fast Gradient Boosting Decision Trees with Bit-Level Data Structures
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    Chapter 36 Shrinkage Estimators for Uplift Regression
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    Chapter 37 String Sanitization: A Combinatorial Approach
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    Chapter 38 Online Linear Models for Edge Computing
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    Chapter 39 Fast Likelihood-Based Change Point Detection
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    Chapter 40 A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting
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    Chapter 41 A Differentially Private Kernel Two-Sample Test
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    Chapter 42 Learning to Signal in the Goldilocks Zone: Improving Adversary Compliance in Security Games
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    Chapter 43 A Stochastic Quasi-Newton Method with Nesterov’s Accelerated Gradient
Attention for Chapter 39: Fast Likelihood-Based Change Point Detection
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