CSE 5243
Transcript Abbreviation:
Intr Data Mining
Course Description:
Knowledge discovery, data mining, data preprocessing, data transformations; clustering, classification, frequent pattern mining, anomaly detection, graph and network analysis; applications.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
(N/A)
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 3241 or 5241; and 2331, 5331, Stat 3301, or ISE 3200; and enrollment in CSE, CIS, ECE, Data Analytics, or ISE major.
Electronically Enforced:
No
Exclusions:
(N/A)
Course Goals / Objectives:
Be competent with anomaly detection algorithms and graph/network analysis algorithms
Master the knowledge discovery process
Be competent with simple data preprocessing and data transformation techniques
Master key classification and clustering algorithms
Master major frequent pattern mining algorithms
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Knowledge Discovery Process and Background | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Elements of Data Preprocessing and Data Transformations | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Data Clustering | 9.0 | 0.0 | 0 | 0.0 | 0 |
| Data Classification | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Frequent Pattern Mining | 7.5 | 0.0 | 0 | 0.0 | 0 |
| Analyzing Graphs and Networks | 7.5 | 0.0 | 0 | 0.0 | 0 |
| Anomaly Detection | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Applications (Bioinformatics, Social Networks) | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homeworks | 20% |
| Midterm | 20% |
| Final Exam | 30% |
| Project/Programming Work | 30% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Introduction to Data Mining | Tan, Steinbach and Kumar, Addison Wesley, 2006 | |
| Data Mining: Concepts and Techniques | J. Han, M. Kamber : Morgan Kaufmann, 2006 |
ABET-CAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| Significant contribution (7+ hours) | 2 | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| Some contribution (1-2 hours) | 3 | Communicate effectively in a variety of professional contexts. |
| Some contribution (1-2 hours) | 4 | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| Significant contribution (7+ hours) | 5 | Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline |
| Significant contribution (7+ hours) | 6 | Apply computer science theory and software development fundamentals to produce computing-based solutions. |
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Significant contribution (7+ hours) | 1 | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| Significant contribution (7+ hours) | 2 | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| Some contribution (1-2 hours) | 3 | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| Some contribution (1-2 hours) | 4 | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| Significant contribution (7+ hours) | 5 | an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
| Significant contribution (7+ hours) | 6 | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| Some contribution (1-2 hours) | 7 | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info: