New Undergraduate Course: COP 4990 – Introduction to Data Mining

Faculty Highlight

COP4990: Introduction to Data Mining
by Dr. Tao Li
Fall 2006, T/Th 12:30pm-1:45pm
Course Schedule

Course Description:
Data is being created faster than we are able to understand and use it. There may be patterns hiding within this data with potentially useful information. Data mining attempts to discover these patterns and unravel this information. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. The goal of this course was for students to develop an understanding of the fundamental principles of data mining, and then apply these concepts to real data, thus gaining a working knowledge of data mining techniques.

Prerequisite: COP3530

Justification:
Large data sets are now being produced and are readily available. Students have access to powerful and fast computers that could be used to mine these data seeking to extract knowledge. However, few courses have been developed at the undergraduate level to lead prepared students to understand the purpose and general methodology of data mining. The focus of traditional database courses is to provide students with a solid foundation on which to build and support operational databases. Data warehousing focuses on the creation of large consolidated databases to aid in the strategic decision-making functions of an organization. Data mining typically focuses on the analysis of these databases in an attempt to find previously unknown associations and patterns in the data. An undergraduate Data Mining course will provide students with exposure to state-of-the-art applications.

Course Content:

  • Data Mining Introduction
  • Data Mining Applications
  • Data Preparation
  • Association Analysis
  • Mining Association Rules
  • Mining Sequential Patterns
  • Mining Temporal Data
  • Mining Spatial Data
  • Mining Graph Patterns
  • Infrequent Patterns Mining
  • Classification and Prediction
  • Clustering
  • Anomaly Detection