Machine Learning for Data Science Handbook

Introduction to Knowledge Discovery and Data Mining.- Preprocessing Methods.- Data Cleansing: A Prelude to Knowledge Discovery.- Handling Missing Attribute Values.- Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour.- Dimension Reduction and Feature Selection.- Discretization Methods.- Outlier Detection.- Supervised Methods.- Supervised Learning.- Classification Trees.- Bayesian Networks.- Data Mining within a Regression Framework.- Support Vector Machines.- Rule Induction.- Unsupervised Methods.- A survey of Clustering Algorithms.- Association Rules.- Frequent Set Mining.- Constraint-based Data Mining.- Link Analysis.- Soft Computing Methods.- A Review of Evolutionary Algorithms for Data Mining.- A Review of Reinforcement Learning Methods.- Neural Networks For Data Mining.- Granular Computing and Rough Sets - An Incremental Development.- Pattern Clustering Using a Swarm Intelligence Approach.- Using Fuzzy Logic in Data Mining.- Supporting Methods.- Statistical Methods for Data Mining.- Logics for Data Mining.- Wavelet Methods in Data Mining.- Fractal Mining - Self Similarity-based Clustering and its Applications.- Visual Analysis of Sequences Using Fractal Geometry.- Interestingness Measures - On Determining What Is Interesting.- Quality Assessment Approaches in Data Mining.- Data Mining Model Comparison.- Data Mining Query Languages.- Advanced Methods.- Mining Multi-label Data.- Privacy in Data Mining.- Meta-Learning - Concepts and Techniques.- Bias vs Variance Decomposition for Regression and Classification.- Mining with Rare Cases.- Data Stream Mining.- Mining Concept-Drifting Data Streams.- Mining High-Dimensional Data.- Text Mining and Information Extraction.- Spatial Data Mining.- Spatio-temporal clustering.- Data Mining for Imbalanced Datasets: An Overview.- Relational Data Mining.- Web Mining.- A Review of Web Document Clustering Approaches.- Causal Discovery.- Ensemble Methods in Supervised Learning.- Data Mining using Decomposition Methods.- Information Fusion - Methods and Aggregation Operators.- Parallel and Grid-Based Data Mining - Algorithms, Models and Systems for High-Performance KDD.- Collaborative Data Mining.- Organizational Data Mining.- Mining Time Series Data.- Applications.- Multimedia Data Mining.- Data Mining in Medicine.- Learning Information Patterns in Biological Databases - Stochastic Data Mining.- Data Mining for Financial Applications.- Data Mining for Intrusion Detection.- Data Mining for CRM.- Data Mining for Target Marketing.- NHECD - Nano Health and Environmental Commented Database.- Software.- Commercial Data Mining Software.- Weka-A Machine Learning Workbench for Data Mining.