• Introduction
• MultiDimensional Data Model
• Association Analysis
• Data Warehouse Architecture
• Capacity Planning
• Q and A
• Search Engine
• Clustering_Code
• Practical Questions
• Outlier finding Clustering Algorithm
• k-means clustering algorithm
• k-medoids clustering algorithm
• k-means vs k-medoids
• DBSCAN
• Divisive Hierarchical Clustering
• Agglomerative hierarchical Clustering
• Limitations of Data Mining
• Predictive and Descriptive Data Mining
• Decision Tree
• Bayesian Classifier
• Association Analysis Algorithms
• Frequent Pattern Growth
• Star Schema
• Data Cube
• OLAP operations
• Linear Classification
• Reconstruction-based Outlier Detection
• Reconstruction-based Outlier Detection using Auto Encoder
• Q_and_A_2025_new
• Data warehouse - Example - i
• Simple - Apriori- First step
• Bayesian Classifier - Concept

Data Mining And Data Warehousing

Star Schema

Star Schema

Components:

• Contains measurable, quantitative data (e.g., sales, revenue).
• Has foreign keys referencing dimension tables.

• Surround the fact table (hence "star").
• Contain descriptive attributes (e.g., date, product, customer, location).
• Used for filtering, grouping, and labeling.

Example:

• Fact Table: Sales (sales_id, product_id, customer_id, date_id, store_id, sales_amount)

Dimension Tables:

• Product (product_id, product_name, category)
• Customer (customer_id, name, region)
• Date (date_id, day, month, year)
• Store (store_id, store_name, location)