skip to main content

Course Websites

CS 412 - Introduction to Data Mining

Spring 2021

Official Description

Concepts, techniques, and systems of data warehousing and data mining. Design and implementation of data warehouse and on-line analytical processing (OLAP) systems; data mining concepts, methods, systems, implementations, and applications. Course Information: 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225.

Related Faculty

Course Director

Text(s)

This is an introductory course on data mining. The course introduces the concepts, algorithms, techniques, and systems of data mining, including (1) what is data mining? (2) get to know your data, (3) data preprocessing, integration and transformation, (4) design and implementation of data warehouse and OLAP systems, (5) data cube technology, (6&7) mining frequent patterns and assoication: basic concepts and advanced methods, (8&9) classification: basic concepts and advanced techniques, and (10) cluster analysis: basic concepts. The course will serve both senior-level computer science undergraduate students and the first-year graduate students interested in the field. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining techniques and data mining systems to analyze large amounts of data.

Learning Goals

Understand the basic principles for data cleaning and data transformation and apply typical methods of data cleaning and transformation in the context of data mining (1), (2), (4), (5), (6)
Understand the basic principles of data warehousing and data cubing and apply typical methods of data warehousing and data cube computation (1), (2), (3), (5), (6)
Understand the basic principles for mining frequent patterns and apply typical frequent pattern mining methods for effective data mining (1), (2), (3, (5), (6)
Understand the basic principles for classification and apply typical classification methods for effective data mining (1), (2), (4), (5), (6)
Understand the basic principles for data clustering and apply typical clustering methods for effective data mining (1), (2), (4), (5), (6)

Topic List

Chapter 1. Introduction

Chapter 2. Know Your Data

Chapter 3. Data Preprocessing

Chapter 4. Data Warehousing and On-Line Analytical Processing

Chapter 5. Data Cube Technology

Chapter 6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods

Chapter 7. Advanced Frequent Pattern Mining

Chapter 8. Classification: Basic Concepts

Chapter 9. Classification: Advanced Methods

Chapter 10. Cluster Analysis: Basic Concepts and Methods

Related-to

The course is related to CS410: Text information systems, CS411 Introduction to database systems and CS467 Machine learning

Assessment and Revisions

Revisions in last 6 years Approximately when revision was done Reason for revision Data or documentation available?
Reorganization of Chapters and split some long Chapters into Two: Basic and Advanced Fall 2010 Working on new version of the textbook which was published in Fall 2011 My 3rd edition of the textbook published in Fall 2011
Reduce the coverage of the last chapter on clustering to basic due to the expansion of other chapters in the new textbook Fall 2011 Students feel too much materials covered and need more focus and in more depath on those covered professional judgement based on students' feedback

Required, Elective, or Selected Elective

Selected Elective.

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Introduction to Data MiningDSO65867ONL4 -    Jiawei Han
Introduction to Data MiningP363461ONL31100 - 1215 T R    
Introduction to Data MiningP463462ONL41100 - 1215 T R