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 Data structures
Algorithm analysis
Relations and sets
Graph theory 5 quarter units. 4 units lecture (200 minutes), 1 unit lab (150 minutes). Selected elective for CS 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. Author's website (textbook errata): http://www.cs.uiuc.edu/~hanj/bk3 Melissa Danforth This course covers the following ACM/IEEE Body of Knowledge student learning outcomes:

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

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)

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

Not applicable to this course. Melissa Danforth on 31 December 2013 Approved by CEE/CS Department on [date]
Effective Winter 2014