Learning Objectives
5 objectives- Understand the fundamental concepts and significance of data mining and its applications.
- Apply data preprocessing techniques to prepare datasets for mining tasks.
- Analyze and implement various classification and clustering algorithms for pattern discovery.
- Explore advanced topics such as association rule mining, anomaly detection, and feature selection.
- Develop skills in pattern recognition and dimensionality reduction to enhance data mining outcomes.
Content Outline
Preview1. 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
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