Data Mining and Knowledge Discovery

  1. Data Preprocessing Fundamentals
    1. Understanding Data Quality
      1. Accuracy Assessment
        1. Completeness Evaluation
          1. Consistency Checking
            1. Timeliness Considerations
              1. Believability Factors
                1. Data Profiling Techniques
                2. Data Cleaning Processes
                  1. Missing Value Handling
                    1. Missing Data Patterns
                      1. Deletion Methods
                        1. Imputation Techniques
                          1. Mean and Median Substitution
                            1. Forward and Backward Fill
                              1. Multiple Imputation
                              2. Noise Reduction
                                1. Noise Identification Methods
                                  1. Smoothing Techniques
                                    1. Binning Approaches
                                      1. Regression-Based Smoothing
                                        1. Clustering for Outlier Detection
                                        2. Inconsistency Resolution
                                          1. Data Validation Rules
                                            1. Constraint Checking
                                              1. Reference Data Validation
                                                1. Cross-Field Validation
                                              2. Data Integration Techniques
                                                1. Multi-Source Data Combination
                                                  1. Schema Integration
                                                    1. Schema Matching Algorithms
                                                      1. Schema Mapping Techniques
                                                        1. Ontology Alignment
                                                        2. Entity Resolution
                                                          1. Duplicate Detection Methods
                                                            1. Record Linkage Algorithms
                                                              1. Similarity Measures
                                                                1. Blocking Techniques
                                                                2. Redundancy Management
                                                                  1. Correlation Analysis
                                                                    1. Covariance Computation
                                                                      1. Statistical Dependency Tests
                                                                    2. Data Reduction Strategies
                                                                      1. Dimensionality Reduction
                                                                        1. Feature Selection Methods
                                                                          1. Filter-Based Selection
                                                                            1. Wrapper-Based Selection
                                                                              1. Embedded Selection
                                                                                1. Univariate Selection
                                                                                  1. Recursive Feature Elimination
                                                                                  2. Feature Extraction Techniques
                                                                                    1. Principal Component Analysis
                                                                                      1. Linear Discriminant Analysis
                                                                                        1. Independent Component Analysis
                                                                                          1. t-Distributed Stochastic Neighbor Embedding
                                                                                            1. Multidimensional Scaling
                                                                                          2. Numerosity Reduction
                                                                                            1. Sampling Techniques
                                                                                              1. Simple Random Sampling
                                                                                                1. Stratified Sampling
                                                                                                  1. Systematic Sampling
                                                                                                    1. Cluster Sampling
                                                                                                    2. Data Aggregation
                                                                                                      1. Histogram Construction
                                                                                                        1. Clustering-Based Reduction
                                                                                                        2. Data Compression
                                                                                                          1. Lossless Compression Methods
                                                                                                            1. Lossy Compression Techniques
                                                                                                              1. Wavelet Transforms
                                                                                                            2. Data Transformation Methods
                                                                                                              1. Normalization Techniques
                                                                                                                1. Min-Max Normalization
                                                                                                                  1. Z-Score Standardization
                                                                                                                    1. Decimal Scaling
                                                                                                                      1. Robust Scaling
                                                                                                                      2. Discretization Approaches
                                                                                                                        1. Equal-Width Binning
                                                                                                                          1. Equal-Frequency Binning
                                                                                                                            1. Entropy-Based Discretization
                                                                                                                              1. Chi-Square-Based Discretization
                                                                                                                              2. Attribute Construction
                                                                                                                                1. Feature Engineering Principles
                                                                                                                                  1. Domain-Specific Features
                                                                                                                                    1. Interaction Features
                                                                                                                                      1. Polynomial Features

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                                                                                                                                  2. Data Types and Sources

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                                                                                                                                  4. Classification Methods