Data Mining and Data Warehousing

Explore the fundamentals of data mining and data warehousing, understand the knowledge discovery process, multi-dimensional data models, and learn about data warehouse architecture and design principles.

Key Topics:

• Overview of Data Mining and Knowledge Discovery Process
• Role and Importance of Data Warehouses
• Key Concepts and Components of Data Mining
• Key Concepts and Components of Data Warehousing
• Multi-Dimensional Data Model – Introduction
• Elements of Multi-Dimensional Data Model
• Steps in Dimensional Modeling
• Multi-Dimensional Schema – Star Schema, Snowflake Schema
• Data Warehouse Architecture
• The 3-Tier Data Warehouse Architecture
• The Bus Architecture
• ETL (Extract, Transform, Load) Process

Master data preprocessing techniques including cleaning, integration, and transformation, understand data warehouse modeling with OLAP operations, and learn frequent pattern mining algorithms like Apriori and FP-Growth.

Key Topics:

• Data Preprocessing – Overview and Importance
• Data Cleaning – Handling Missing Values and Noise
• Data Integration and Schema Integration
• Data Reduction Techniques
• Data Transformation and Discretization
• Data Warehouse Modeling – Data Cube
• Typical OLAP Operations – Roll-up, Drill-down, Slice, Dice, Pivot
• Role of Concept Hierarchies
• OLAP Server Architectures – ROLAP, MOLAP, HOLAP
• Mining Frequent Patterns – Basic Concepts
• Frequent Itemset Mining – The Apriori Algorithm
• Generating Association Rules from Frequent Itemsets
• FP-Growth Algorithm

Learn classification methods including decision tree induction, Bayesian classification, rule-based classification, and understand model evaluation techniques for assessing classifier performance.

Key Topics:

• Classification – General Approach to Solving Classification Problems
• Classification by Decision Tree Induction
• Attribute Selection Measures – Information Gain, Gini Index, Gain Ratio
• Tree Pruning Techniques
• Bayesian Classification
• Bayes' Theorem and Naive Bayes Classifier
• Bayesian Belief Networks
• Rule-Based Classification
• Sequential Covering Algorithm
• Model Evaluation and Selection
• Holdout Method and Cross-Validation
• Confusion Matrix and Accuracy Metrics
• Precision, Recall, and F1-Score

Explore cluster analysis methods including partitioning, hierarchical, density-based, and grid-based clustering algorithms, and understand ethical considerations and privacy-preserving techniques in data mining.

Key Topics:

• Cluster Analysis – Introduction and Applications
• Types of Clustering Methods
• Partitioning Methods – K-Means Clustering Algorithm
• K-Medoids Clustering (PAM Algorithm)
• Hierarchical Methods – Agglomerative and Divisive
• BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
• Density-Based Methods – DBSCAN Algorithm
• DBSCAN Parameters – Eps and MinPts
• Grid-Based Methods – STING (Statistical Information Grid)
• CLIQUE Algorithm
• Cluster Validation and Evaluation
• Data Mining Ethics and Privacy
• Ethical Considerations in Data Mining
• Privacy-Preserving Data Mining Techniques
• Data Anonymization and k-Anonymity