Algorithms Seminar/Spring09

(Difference between revisions)

Revision as of 03:00, 13 February 2009

Spring 2009: CS 7936: Clustering

Fri 10:45 - 12:05 | WEB 1460

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)

Date	Paper(s)	Presenter
Clustering via proximity
Jan 16	Introduction to Clustering. Understanding different distance measures.	Suresh
Jan 23	K-means: Lloyd's algorithm, worst-case behaviour, and an asymptotic analysis	Adam
Jan 30	Hierarchical Methods 1 2; (This chapter from the IR book)	Avishek
Clustering via (dis)similarity
Feb 6	Correlation clustering: the original paper, and an improved algorithm (only the clustering section). Also see Claire Mathieu's blog post	Jagadeesh
Feb 13	Spectral Clustering; Link on Wikipedia Applications of Graph Laplacians	Nathan
Clustering as model building
Feb 20	EM, a two-round variant for Gaussians	Piyush
Feb 27	Choosing k: the elbow method, the A,B,D information criteria, and an information-theoretic approach (how humans estimate k)	John M & Pravin C
Mar 6	Information-theoretic clustering: IB, RIC	Arvind
Large-Data clustering
Mar 13	BIRCH and CURE	Raj Varma
Mar 27	Stream Clustering; Better Streaming Algorithms; Clustering Data Streams	Amit
Comparing Clusterings
Apr 3	Metrics on Clusterings	Ruihong
Apr 10	Consensus Clustering : Cluster Ensembles, Approximations for Consensus Clustering?,	Parasaran
Meta-clustering
Apr 17	Axioms of Clustering: Problems, and a working set	Seth
Apr 24	Cluster Stability; Sober look at Cluster Stability; Stability for Finite Samples	Jiarong
May 1	Clusterability and the efficency of clustering	Thanh

Clustering with differently shaped clusters (subspace/projective clustering)
methods for soft clustering
high and low dimensional approximation schemes for clustering problems.
manifold clustering
"reclustering": given a clustering, find a new one that has some relationship to it.
biclustering (or co-clustering): cluster the rows and columns of a matrix.
Clustering with Outliers

@@ Line 16: / Line 16: @@
 * [http://www.cs.utah.edu/~suresh Suresh Venkatasubramanian], Assistant Professor, School of Computing
 * [http://www.cs.utah.edu/~praman Parasaran Raman], PhD Student, School of Computing
-* [mailto:john.moeller@utah.edu John Moeller], Non-matriculated Student, School of Computing
+* [mailto:john.moeller@utah.edu John Moeller], PhD Student, School of Computing
 * [http://www.cs.utah.edu/~ngilbert Nathan Gilbert], PhD Student, School of Computing
 * [mailto:rajvarma@cs.utah.edu Raj Varma Kommaraju], MS Student, School of Computing