ACS 57700 - Knowledge Discovery And Data Mining

Data mining has emerged as one of the most exciting and dynamic fields in computer science. With an explosive growth in computer and database technology, the huge amount of data has been collected. Data mining is the process to extract interesting and novel knowledge from large amount of data. ACS 57700 is designed to provide graduate students a broad background in the design and use of data mining algorithms, exposure to software tools, specialized expertise in applying these ideas to a real-life situation through a term project. Topics include data preprocess, data exploration, frequent pattern mining, classification and clustering analysis.

Cr. 3.
Student Learning Outcomes

1. Learning fundamental principles about computational techniques for data preparation (e.g., data cleaning, data integration, and data transformation) and data exploration.
2. Learning principles about computational techniques to discover frequent patterns (e.g., association rule patterns, sequential patterns) in data collection.
3. Learning principles about computational approaches for classification, i.e., machine learning techniques for data mining (e.g., decision tree induction, rule-based classifiers, Bayesian classifiers)
4. Learning principles about computational approaches for clustering (e.g., partitional clusters, hierarchical clusters, density-based clusters).
5. Exposing research papers for knowing current data mining research trends.
6 Learning a data mining software tool for hands-on experience.
7. Developing creative capacities (knowledge, critical thinking, communication, etc.) to apply course material for analyzing a variety of real-world data and evaluating discovered patterns or built models.