Data Mining And Pattern Recognition

1. Introduction to Data Mining

1.1 Definition and Basic Concepts

• What is data mining?
• Key terminology

1.2 Importance and Applications

• Business intelligence
• Healthcare
• Finance
• Marketing

1.3 Data Mining Process

• Data collection
• Data preprocessing
• Data mining techniques
• Interpretation and evaluation

2. Data Preprocessing

2.1 Data Cleaning

• Handling missing values
• Noise reduction
• Data inconsistency

2.2 Data Transformation

• Normalization (min-max, z-score)
• Aggregation
• Generalization

2.3 Attribute Selection

• Feature selection methods
• Dimensionality considerations

3. Classification Algorithms

3.1 Decision Trees

• Structure and construction
• Entropy and information gain

3.2 Support Vector Machines (SVM)

• Margin maximization
• Kernel functions

3.3 k-Nearest Neighbors (k-NN)

• Distance metrics
• Choosing k

3.4 Naive Bayes

• Bayes theorem
• Assumptions and applications

4. Clustering Techniques

4.1 K-means Clustering

• Algorithm steps
• Choosing number of clusters

4.2 Hierarchical Clustering

• Agglomerative vs divisive
• Dendrogram interpretation

4.3 DBSCAN

• Density-based clustering
• Parameters: epsilon and minPts

5. Association Rule Mining

5.1 Fundamentals

• Support, confidence, lift

5.2 Apriori Algorithm

• Candidate generation
• Pruning strategies

5.3 FP-Growth Algorithm

• FP-tree construction
• Mining frequent itemsets

6. Anomaly Detection

6.1 Concept of Anomalies

• Types of anomalies

6.2 Outlier Detection Techniques

• Statistical methods
• Distance-based methods

6.3 One-Class Classification

• Support vector data description (SVDD)
• Applications

7. Feature Selection and Dimensionality Reduction

7.1 Feature Selection

• Filter, wrapper, and embedded methods
• Feature importance ranking

7.2 Dimensionality Reduction

• Principal Component Analysis (PCA)
• Other techniques overview (t-SNE, LDA)

8. Pattern Recognition

8.1 Fundamentals

• Definitions and scope

8.2 Pattern Matching

• String and sequence matching

8.3 Pattern Classification

• Supervised pattern recognition

8.4 Pattern Clustering

• Unsupervised pattern recognition