Data Mining and Data Warehousing (BTCS 702-18)

Course Objectives

This course aims to:

• Introduce the fundamental concepts and techniques of data mining and data warehousing.
• Provide an understanding of various data preprocessing and data mining algorithms.
• Enable students to apply data mining techniques to extract knowledge and patterns from large datasets.
• Familiarize students with the design and implementation of data warehouses.

Course Outcomes

Upon completion of this course, students will be able to:

1. Understand the principles of data mining and data warehousing.
2. Preprocess and transform data for effective analysis.
3. Apply various data mining algorithms to discover patterns and insights.
4. Design and implement a data warehouse for decision support.
5. Evaluate the performance of different data mining techniques.

Detailed Syllabus

Module 1: Introduction to Data Mining

• Motivation and definition of data mining
• Kinds of data and patterns
• Major issues in data mining

Module 2: Data Preprocessing

• Data cleaning
• Data integration
• Data reduction
• Data transformation

Module 3: Data Warehousing and OLAP

• Data warehouse
• Multidimensional data model
• OLAP operations
• Data cube aggregation
• Data warehouse implementation

Module 4: Association Rules and Classification

• Association rule mining:
  • Frequent itemset generation
  • Rule generation
• Classification:
  • Decision tree induction
  • Bayesian classification
  • Rule-based classification
  • Model evaluation and selection

Module 5: Clustering

• Clustering:
  • Partitioning methods (k-means, k-medoids)
  • Hierarchical methods
  • Density-based methods (DBSCAN)
  • Grid-based methods
  • Model evaluation

Module 6: Other Data Mining Tasks

• Anomaly detection (Outlier analysis)
• Web mining
• Spatial-temporal data mining
• Social network analysis
• Text mining

Text Books

• Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber
• Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar

Reference Books

• Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten and Eibe Frank
• The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling by Ralph Kimball and Margy Ross