Title:  A generalized form of K-means
Speaker: George Tseng

Abstract:
In this talk a class of loss function extended from K-means
criterion is introduced for clustering.

Two major extensions are involved: penalization and weighting.
The additive penalty term is used to allow a set of noise (scattered) objects without
being clustered. Weights are introduced to account for prior information of preferred or
prohibited cluster patterns to be found. 

Their relationships with classification likelihood of Gaussian models are explored.
Applications on simulated data as well as real data from
microarray and tandem mass spectrometry experiments are evaluated
to demonstrate its flexibility and applicability to clustering large complex data.

Following the talk Prof. Haiyan Huang is organizing dinner with the speaker.
Please contact hhuang at stat dot Berkeley dot EDU if you wish to join dinner.