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DTM-5350 Data Mining and Knowledge Management

This course will serve to introduce students to data mining and knowledge management. Data mining (DM) is concerned with the discovery of "hidden" knowledge in large data sets. This knowledge represents one aspect of an organization's intellectual capital and is often expressed in the form of trends or major themes that reoccur in the data. Knowledge management (KM) systems are designed to exploit the results of data mining and facilitate the analysis and evaluation of both tangible and intangible knowledge assets. In this course students will explore data mining methods used for prediction and knowledge discovery. These methods include regression, nearest neighbor, clustering, K-means, decision trees, association rules, and neural networks. In addition, students will become familiar with the current theories, practices, tools, and techniques used to management knowledge assets.

Credits: 3

Offered in Jan 2024, May 2024, Sep 2024, Mar 2025

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(Please visit the University bookstore to view the correct materials for each course by semester as the contents of the actual online syllabus may differ from the preview due to updates or revisions)