CMPS 445 Data Mining and Visualization
Catalog Description
CMPS 445 Data Mining and Visualization (5)
Knowledge discovery in and visualization of large datasets, including data warehouses and text-based information systems. Topics covered include data mining concepts, information retrieval, analysis methods, storage systems, visualization, implementation and applications. Prerequisite: CMPS 312
Prerequisites by Topic
Data structures
Algorithm analysis
Relations and sets
Graph theory
Units and Contact Time
5 quarter units. 4 units lecture (200 minutes), 1 unit lab (150 minutes).
Selected elective for CS
Required Textbook
Data Mining: Concepts and Techniques, Third edition. Jiawei Han, Micheline Kamber, and Jian Pei. Morgan Kaufmann Publishers, 2012, ISBN-13 978-0-12-381479-1.
Recommended Textbook and Other Supplemental Materials
Author's website (textbook errata):
Melissa Danforth
Student Learning Outcomes
This course covers the following ACM/IEEE Body of Knowledge student learning outcomes:

CC-IS: Intelligent Systems
CC-IM: Information Management

ABET Outcome Coverage
The course maps to the following performance indicators for Computer Science (CAC/ABET):
(CAC PIa1): Apply and perform the correct mathematical analysis.
Apply statistical methods to perform introductory and intermediate data mining analysis, such as pattern mining, classification, and clustering.
(CAC PIb1): Identify key components and algorithms necessary for a solution.
Analyze a common data mining problem, such as one of the national data mining competition challenges, and select the appropriate data mining technique(s) to solve that problem.
(CAC PIb2): Produce a solution within specifications.
Implement the chosen data mining technique(s) to solve a data mining problem using a high level programming language and/or a data mining toolkit.
(CAC PIe1): Recognize ethical issues involved in a professional setting.
Explore the ethical dimensions of data mining, such as access to personal data and human subjects research, and analyze ethical dilemmas involving data mining. (Also part of the CSUB Ethics Across the Curriculum initiative)
Lecture Topics and Rough Schedule

Not in Book Ethics of Data Mining Weeks 1 and 2
Chapter 1 Introduction Week 1
Chapter 2 Getting to Know Your Data Weeks 1 and 2
Chapter 3 Data Preprocessing Weeks 2 and 3
Chapter 6 Mining Frequent Patterns: Basic Concepts Weeks 3 to 5
Chapter 7 Advanced Pattern Mining (selected topics) Week 5
Chapter 8 Classification: Basic Concepts Weeks 5 to 7
Chapter 9 Classification: Advanced Methods Weeks 7 and 8
Chapter 10 Cluster Analysis: Basic Concepts Weeks 8 and 9
Chapter 11 Advanced Cluster Analysis (selected topics) Weeks 9 and 10
Chapter 12 Outlier Detection (selected topics) Week 10
Chapter 4 Data Warehousing (selected topics) Week 10
Design Content Description
Not applicable to this course.
Prepared By
Melissa Danforth on 31 December 2013
Approved by CEE/CS Department on [date]
Effective Winter 2014