##### Department of Mathematics,

University of California San Diego

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### Probability and Statistics Colloquium

## Bruno Pelletier

#### Univ. Montpellier II

## Clustering with level sets

##### Abstract:

The objective of clustering, or unsupervised classification, is to partition a set of observations into different groups, or clusters, based on their similarities. Following Hartigan, a cluster is defined as a connected component of an upper level set of the underlying density. In this talk, we introduce a spectral clustering algorithm on estimated level sets, and we establish its strong consistency. We also discuss the estimation of the number of connected components of density level sets.

Host: Dimitris Politis

### March 3, 2009

### 12:00 PM

### AP&M 6402

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