Data Warehousing and Data Mining Lecture Notes

Course Overview

• Objective: Learn principles of data warehousing and data mining concepts.
• Focus Areas:
  • Data warehouse design
  • Data mining concepts
  • Association rules
  • Classification algorithms
  • Clustering techniques

Unit I: Data Warehousing

Introduction to Data Warehouse

• Definition: Subject-oriented, integrated, time-variant, non-volatile data collection.
• Key Characteristics:
  • Subject-Oriented: Analyzes specific subject areas.
  • Integrated: Combines data from various sources.
  • Time-Variant: Stores historical data.
  • Non-Volatile: Data does not change once entered.

Data Warehouse Design Process

• Approaches:
  • Top-Down: Overall design and planning.
  • Bottom-Up: Prototypes and experiments.
  • Combined: Integrates both strategies.
• Steps:
  • Choose business process.
  • Select grain, dimensions, and measures.

Differences with Operational Database Systems

• Operational databases focus on current operations, while data warehouses focus on historical analysis.

Data Warehouse Architecture

• Three Tiers:
  • Tier 1: Warehouse database server.
  • Tier 2: OLAP server (ROLAP/MOLAP).
  • Tier 3: Client layer with tools for analysis.

Schema Design

• Types:
  • Star Schema: Central fact table with dimension tables.
  • Snowflake Schema: Normalized dimension tables.
  • Fact Constellation: Multiple fact tables.

Unit II: Fundamentals of Data Mining

Introduction to Data Mining

• Goal: Extract useful information and transform it into understandable structures.
• Key Properties: Pattern discovery, prediction, actionable information.

Data Mining Functionalities

• Tasks:
  • Descriptive: General properties of data.
  • Predictive: Inference to make predictions.

Data Preprocessing

• Importance: Ensures data quality and reliability.
• Techniques:
  • Data Cleaning
  • Data Integration
  • Data Transformation
  • Data Reduction

Unit III: Association Rules

Association Rule Mining

• Purpose: Discover interesting relations between variables in large databases.
• Applications: Market Basket Analysis.

Apriori Algorithm

• Process: Iterative approach using candidate generation.

FP-Growth Algorithm

• Approach: Frequent pattern growth without candidate generation.

Unit IV: Classification

General Approaches

• Data Preparation: Cleaning, relevance analysis, transformation.

Decision Trees

• Algorithm: Uses splitting criteria to classify data.

Bayesian Classification

• Types:
  • Naive Bayesian Classifier
  • Bayesian Belief Networks

K-Nearest Neighbor (kNN)

• Characteristics: Uses distance metrics, assigns class based on nearest neighbors.

Unit V: Clustering

Overview

• Definition: Grouping similar data objects into clusters.
• Applications: Market research, pattern recognition, outlier detection.

Clustering Methods

• Types:
  • Partitioning Methods
  • Hierarchical Methods
  • Density-Based Methods
  • Grid-Based Methods

Outlier Analysis

• Purpose: Detect data objects that do not comply with general data behavior.

Textbooks and References

• Textbooks:
  • "Data Mining - Concepts and Techniques" by Jiawei Han and Micheline Kamber.
  • "Introduction to Data Mining" by Pang-Ning Tan, Vipin Kumar, and Michael Steinbach.
• Reference Books:
  • "Data Mining Techniques" by Arun K Pujari.
  • "Data Warehousing Fundamentals" by Paulraj Ponnaiah.
  • "The Data Warehouse Lifecycle Toolkit" by Ralph Kimball.