Concept

Correlation clustering

Related publications (158)

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Appearance-based Keypoint Clustering

Pascal Fua, Sabine Süsstrunk, Vincent Lepetit, Francisco Estrada

We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that be ...

2009

Flickr Hypergroups

Daniel Gatica-Perez, Radu Andrei Negoescu

The amount of multimedia content available online constantly increases, and this leads to problems for users who search for content or similar communities. Users in Flickr often self-organize in user communities through Flickr Groups. These groups are part ...

2009

Flickr Hypergroups

Daniel Gatica-Perez, Radu Andrei Negoescu

Idiap2009

A Clustering Method Based on Soft Learning of Model (Prototype) and Dissimilarity Metrics

Arash Arami

Many clustering methods are designed for especial cluster types or have good performance dealing with particular size and shape of clusters. The main problem in this connection is how to define a similarity (or dissimilarity) criterion to make an algorithm ...

Springer-Verlag2009

Fast high-dimensional Bayesian classification and clustering

Vahid Partovi Nia

We introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single ...

EPFL2009

Short Term Travel Time Prediction Using Floating Car Data Based On Cluster Analysis

Precise short-term prediction of traffic parameters such as flow and travel-time is a necessary component for many ITS applications. This work describes the research on a novel, fast, and robust algorithm which is based on a partitioning cluster analysis. ...

2008

Data mining methodologies for supporting engineers during system identification

Sandro Saitta

Data alone are worth almost nothing. While data collection is increasing exponentially worldwide, a clear distinction between retrieving data and obtaining knowledge has to be made. Data are retrieved while measuring phenomena or gathering facts. Knowledge ...

EPFL2008

Blind Audiovisual Source Separation Using Sparse Redundant Representations

Pierre Vandergheynst, Gianluca Monaci, Anna Llagostera Casanovas

In this work, we present a method that jointly separates active audio and visual structures on a given mixture. This new concept, the Blind Audiovisual Source Separation (BAVSS), is achieved by exploiting the coherence existing between the recorded signal ...

2007

Learning Cluster Type and Dissimilarity Metric for Each Cluster Using a Set of Possible Cluster Types

Arash Arami

One of the shortcomings of the existing clustering methods is their problems dealing with different shape and size clusters. On the other hand, most of these methods are designed for especial cluster types or have good performance dealing with particular s ...

2007

Likelihood estimation of the extremal index

Mária Süveges

The extension of the likelihood method of Süveges (Extremes, 2007) is presented. The extension allows for finding independent clusters of extreme events and determining the range of dependence on extremal levels, and estimate clustering characteristic of t ...

2007