CMPS 4450 Data Mining and Visualization
Catalog Description
CMPS 4450 Data Mining and Visualization (4)
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 3120
Prerequisites by Topic
Data structures
Algorithm analysis
Relations and sets
Graph theory
Units and Contact Time
4 semester units. 3 units lecture (150 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 CS2013 (Computer Science) Body of Knowledge student learning outcomes:

CS-IM/Data Mining
CS-IS/Basic Search Strategies
CS-IS/Reasoning Under Uncertainty
CS-IS/Advanced Machine Learning

ABET Outcome Coverage
The course maps to the following performance indicators for Computer Science (CAC/ABET):
3a. An ability to apply knowledge of computing and mathematics appropriate to the discipline.
3e. An understanding of professional, ethical, legal, security, and social issues and responsibilities.
3g. An ability to analyze the local and global impact of computing on individuals, organizations, and society.
Lecture Topics and Rough Schedule
1Outside information and Chapter 1 Ethics of data mining, Introduction to data mining
2Chapter 2 Data characteristics, Visualization, Similarity and dissimilarity measures
3 and 4Chapter 3 Data preprocessing, reduction, and transformation
5Chapter 6 Patterns, correlations and associations
6Chapter 6 Rule generation, Evaluating generated association rules
7Chapters 7 and 8 Overview of advanced pattern mining, Classification introduction
8Chapter 8 Decision trees, Naive Bayesian classifier, Rule-based classifier
9Chapters 8 and 9 Evaluating classifiers, Bayesian belief networks
10Chapter 9 Support vector machines, k-nearest neighbor, Additional classifiers
11Chapter 10 Clustering, k-means, k-metroids, Hierarchical clustering
12Chapter 10 Density clustering, Grid-based clustering, Evaluating clusters
13Chapter 11 Fuzzy clustering, Clustering with constraints
14Chapter 12 Outlier detection
15Chapters 4 and 5 Data warehousing and views
Design Content Description
Not applicable to this course.
Prepared By
Melissa Danforth on 31 July 2014
Approved by CEE/CS Department on [date]
Effective Fall 2016