Add to ORKGWe present a novel spectral clustering method that enables users to incor-porate prior knowledge of the size of clusters into the clustering process. The cost function, which is named size regularized cut (SRcut), is defined as the sum of the inter-cluster similarity and a regularization term mea-suring the relative size of two clusters. Finding a partition of the data set to minimize SRcut is proved to be NP-complete. An approximation algo-rithm is proposed to solve a relaxed version of the optimization problem as an eigenvalue problem. Evaluations over different data sets demon-strate that the method is not sensitive to outliers and performs better than normalized cut.
Data clustering is a frequently used technique in finance, computer science, and engineering. In mos...
Spectral clustering is a powerful clustering method for document data set. However, spectral cluster...
Abstract. We present a set of clustering algorithms that identify cluster boundaries by searching fo...
Spectral clustering (SC) is a popular and versatile clustering method based on a relaxation of the n...
Abstract. Clustering is of interest in cases when data are not labeled enough and a prior training s...
Normalized Cuts is a state-of-the-art spectral method for clustering. By apply-ing spectral techniqu...
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity...
The performance of spectral clustering can be considerably improved via regularization, as demonstra...
Spectral clustering enjoys its success in both data clustering and semi-supervised learning. But, mo...
In this paper, we propose a new spectral clustering method, referred to as Spectral Embedded Cluster...
In many applications, we need to cluster large-scale data objects. However, some recently proposed c...
Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by ...
Clustering is a fundamental task in machine learning and data analysis. A large number of clustering...
In this paper we introduce a new clustering technique called Regularity Clustering. This new techniq...
AbstractClustering is a widely used technique in machine learning, however, relatively little resear...
Data clustering is a frequently used technique in finance, computer science, and engineering. In mos...
Spectral clustering is a powerful clustering method for document data set. However, spectral cluster...
Abstract. We present a set of clustering algorithms that identify cluster boundaries by searching fo...
Spectral clustering (SC) is a popular and versatile clustering method based on a relaxation of the n...
Abstract. Clustering is of interest in cases when data are not labeled enough and a prior training s...
Normalized Cuts is a state-of-the-art spectral method for clustering. By apply-ing spectral techniqu...
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity...
The performance of spectral clustering can be considerably improved via regularization, as demonstra...
Spectral clustering enjoys its success in both data clustering and semi-supervised learning. But, mo...
In this paper, we propose a new spectral clustering method, referred to as Spectral Embedded Cluster...
In many applications, we need to cluster large-scale data objects. However, some recently proposed c...
Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by ...
Clustering is a fundamental task in machine learning and data analysis. A large number of clustering...
In this paper we introduce a new clustering technique called Regularity Clustering. This new techniq...
AbstractClustering is a widely used technique in machine learning, however, relatively little resear...
Data clustering is a frequently used technique in finance, computer science, and engineering. In mos...
Spectral clustering is a powerful clustering method for document data set. However, spectral cluster...
Abstract. We present a set of clustering algorithms that identify cluster boundaries by searching fo...