Algorithms Seminar/Spring09
From ResearchWiki
Spring 2009: CS 7936: Clustering
Fri 10:45 - 12:05 | WEB 1460
Contents |
Course Materials
Seminar format and grading
- Student presentations on material selected by me. Please read, reflect upon, and follow these presentation guidelines
- One week before presentation is scheduled: student meets with me to discuss content of the presentation
- Day before presentation: student submits summary (either notes, or slides for presentation)
- Day before presentation: non-presenters submit questions on the material
- Day after presentation: questions are addressed by presenter or questioner (on the wiki talk page)
Participants
- Suresh Venkatasubramanian, Assistant Professor, School of Computing
- Parasaran Raman, MS Student, School of Computing
- John Moeller, Non-matriculated Student, School of Computing
Topics
Other topics that need to be covered:
- Spectral Clustering
- Stream clustering
- large-data clustering
- categorical clustering
Schedule
| Date | Paper(s) | Presenter | Date | Paper(s) | Presenter |
|---|---|---|---|---|---|
| Algorithms | |||||
| Jan 16 | Introduction to Clustering. Understanding different distance measures. | Jan 23 | Different Types of existing Clustering Algorithms. | ||
| Jan 30 | The K – Means, K - Center and Hierarchical clustering techniques | Feb 6 | Expectation Maximization | ||
| Feb 13 | Text Clustering | Feb 20 | Clustering Evaluation : Comparison of Clusterings | ||
| Feb 27 | Consensus Clustering - I | Mar 6 | Consensus Clustering - II | ||
| Mar 13 | Soft Clustering | Mar 20 | Conceptual Clustering | ||
| Mar 27 | Correlation Clustering | Apr 3 | Axioms of Clustering | ||
| Apr 10 | Clustering : Discussing Real-time Application – I | Apr 17 | Clustering : Discussing Real-time Application – II | ||
| Apr 24 | Project Presentation | ||||
Paper Summaries
Project Ideas
Course Links
1. Understanding distance measures : Cluster Analysis, Basic Clustering Concepts
2. Clustering Algorithms : Tutorial on Clustering Algorithms, Basic Clustering Techniques
3. K-Means, K-Center and Hierarchical Clustering: K Means, K Center, Hierarchical Clustering
4. Expectation Maximization: EM on Wikipedia, EM Algorithm
5. Text Clustering: Ontology-based Distance Measure for Text Clustering, Text Clustering, Frequent Term-Based Text Clustering
6. Clustering Evaluation : Comparison of Clusterings - Information based Distance
7. Consensus Clustering-I: Clustering Aggregation, Weighted Consensus Clustering
8. Consensus Clustering-II: Consensus Clustering Algorithms: Comparison and Refinement, Cluster Ensembles
9. Soft Clustering: Soft Clustering Ensembles, Soft Clustering on Graphs
10. Conceptual Clustering: Conceptual Clustering Framework, Data Mining using Conceptual Clustering
11. Correlation Clustering: [1]
12. Axioms of Clustering: Impossibility theorem for Clustering
Past Semesters
- Fall 2007: Approximate High Dimensional Geometry
- Spring 2008: The Geometry of Information Spaces
- Fall 2008: Randomization
