Algorithms Seminar/Spring09

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Revision as of 06:10, 1 March 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
Feb 20	Spectral Clustering; Link on Wikipedia Applications of Graph Laplacians (continued)	Thanh
Clustering as model building
Feb 27	Gaussian Mixture Models and EM, a two-round variant for Gaussians	Piyush
Mar 6	Information-theoretic clustering: IB, RIC	Arvind
Mar 13	Choosing k: the elbow method, the A,B,D information criteria, and an information-theoretic approach (how humans estimate k)	John M & Pravin C
Large-Data clustering
Mar 27	BIRCH and CURE	Raj Varma
Apr 3	Stream Clustering; Better Streaming Algorithms; Clustering Data Streams	Amit
Comparing Clusterings
Apr 10	Metrics on Clusterings	Ruihong
Apr 17	Consensus Clustering : Cluster Ensembles, Approximations for Consensus Clustering?,	Parasaran
Meta-clustering
Apr 24	Axioms of Clustering: Problems, and a working set	Seth
May 1	Cluster Stability; Sober look at Cluster Stability; Stability for Finite Samples	Jiarong

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 57: / Line 57: @@
 | colspan="3" bgcolor="#dddddd" style = "text-align:center" | '''Clustering as model building'''
 |-
-| Feb 27 || [http://en.wikipedia.org/wiki/Expectation-maximization_algorithm EM], [http://www.cse.ucsd.edu/~dasgupta/papers/em.ps a two-round variant for Gaussians] || Piyush
+| Feb 27 || [http://www.cise.ufl.edu/class/cap6610sp07/mix_gauss.pdf Gaussian Mixture Models and EM], [http://www.cse.ucsd.edu/~dasgupta/papers/em.ps a two-round variant for Gaussians] || Piyush
 |-
 | Mar 6 || Information-theoretic clustering: [http://en.wikipedia.org/wiki/Information_bottleneck_method IB], [http://www.cs.cmu.edu/~jypan/publications/kdd06RIC.pdf RIC]|| Arvind