A Data Mining Course for Computer Science and non Computer Science Students

1. A Data Mining Course for Computer Science and non Computer Science Students Jamil Saquer Computer Science Department Missouri State University Springfield, MO

2. Outline • Introduction • Motivation • Challenges • Design of the Course • Topics Covered • Assignments • Examination Format • Conclusion

3. Introduction • What is data mining (DM)? • non-trivial process of identifying valid, novel, useful, and ultimately understandable patterns in large volumes of data. • DM is an interdisciplinary topic • Has many things in common with machine learning and pattern recognition

4. Motivation for the Course • Introducing more electives • Introducing graduate level CS courses • Informatics Program • Interest to faculty members and students from other departments • Author’s main area of research

5. Challenges in Designing the Course • Diverse student population • CS vs. non-CS • undergrad vs. grad • Solution • Informatics program in design stages • MNAS CS option is new • Therefore, emphasis on undergrad CS students

6. Accommodating other students • Minimize prerequisites • CS 2 (or even CS 1) • Capable of using a DM software • Scientific background/mentality • One from business, another from GGP • For grad CS students: • project requires more research • Tests could be a little different • Emphasize understanding basic DM concepts and using software for mining data

7. Design of the Course • Used book by Dunham • Book divided into 3 parts • About 1 week spent on definitions, applications, motivations, challenges, … • Core of the course spent on core DM subjects: classification, clustering, mining association rules • Last week for project presentations

8. Classification • Assigning objects to classes • supervised learning • Example: classify a military vehicle as a friendly or an enemy vehicle • Methods covered include: decision trees, Naïve Bayesian, k-nearest neighbor, backpropogation

9. Clustering • Grouping objects into different classes • unsupervised learning • Example: cluster Weblog data to discover groups of similar access patterns • Techniques covered include: link algorithms, nearest neighbor, k-means, PAM, BIRCH, DBSCAN, CURE, ROCK

10. Association Rules • Finding patterns that occur together • Example: diapers and beer are usually bought together • Techniques covered: Apriori, sampling, partitioning, FP-growth

11. Assignments • Students need to learn how to mine data • One assignment on each core DM topic • apply two different algorithms on at least two data sets, one has to be relatively large • can use any DM package (Weka) • Students write a report • Students learn how to run an experiment

12. Term Project • Group projects • Either provide a non-trivial implementation of a DM algorithm • Or, learn about a DM topic not discussed in class • Graduate students required to read at least three research papers and to write a report • All students present their project in class

13. Examination Format • Open book • Two types of questions • First type, require basic knowledge of the material • definitions, T/F, short answers • Second type, apply certain algorithms on small data sets

14. Conclusion • DM is an interesting course for CS and non-CS students • DM can be taught for non-CS students • A DM course can be taught for students with minimal CS background

15. Questions