Add to ORKGWe present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, which results in a family of objective functions with a natural geometric interpretation. We give efficient algorithms for calculating the continuous regularization path of solutions, and discuss relative advantages of the parameters. Our method experimentally gives state-of-the-art results similar to spectral clustering for non-convex clusters, and has the added benefit of learning a tree structure from the data. Contents
Most sparse or low-rank-based subspace clustering methods divide the processes of getting the affini...
Spectral clustering is a fundamental technique in the field of data mining and information processin...
<p>Convex clustering of the HGDP data using a small number <i>k</i> of nearest neighbors to resolve ...
International audienceWe present a new clustering algorithm by proposing a convex relaxation of hier...
This paper proposes an exceptionally simple algorithm, called forward-stagewise clustering, for conv...
The main purpose of this dissertation is to demonstrate that using a robust loss function (instead o...
Abstract: Fast accumulation of large amounts of complex data has cre-ated a need for more sophistica...
k-means clustering is a popular approach to clustering. It is easy to implement and intuitive but ha...
The primary goal in cluster analysis is to discover natural groupings of objects. The field of clust...
We present a novel linear clustering framework (DIFFRAC) which relies on a lin-ear discriminative co...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
In recent years, spectral clustering has become a standard method for data analysis used in a broad ...
Fast accumulation of large amounts of complex data has created a needfor more sophisticated statisti...
The Graph-based Convex Clustering (GCC) method has gained increasing attention recently. The GCC met...
We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yiel...
Most sparse or low-rank-based subspace clustering methods divide the processes of getting the affini...
Spectral clustering is a fundamental technique in the field of data mining and information processin...
<p>Convex clustering of the HGDP data using a small number <i>k</i> of nearest neighbors to resolve ...
International audienceWe present a new clustering algorithm by proposing a convex relaxation of hier...
This paper proposes an exceptionally simple algorithm, called forward-stagewise clustering, for conv...
The main purpose of this dissertation is to demonstrate that using a robust loss function (instead o...
Abstract: Fast accumulation of large amounts of complex data has cre-ated a need for more sophistica...
k-means clustering is a popular approach to clustering. It is easy to implement and intuitive but ha...
The primary goal in cluster analysis is to discover natural groupings of objects. The field of clust...
We present a novel linear clustering framework (DIFFRAC) which relies on a lin-ear discriminative co...
The problem of cluster analysis is formulated as a problem of non-smooth, non-convex optimization, a...
In recent years, spectral clustering has become a standard method for data analysis used in a broad ...
Fast accumulation of large amounts of complex data has created a needfor more sophisticated statisti...
The Graph-based Convex Clustering (GCC) method has gained increasing attention recently. The GCC met...
We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yiel...
Most sparse or low-rank-based subspace clustering methods divide the processes of getting the affini...
Spectral clustering is a fundamental technique in the field of data mining and information processin...
<p>Convex clustering of the HGDP data using a small number <i>k</i> of nearest neighbors to resolve ...