Add to ORKGWe develop a weighted kernel k-means approach for clustering using the Sum-Over-Forests density index as node weights. Furthermore, we implement and test the developed algorithm and vary the used kernels to ascertain the algorithms functionality. The algorithm gives good results, but is strongly dependent on the kernel parameters as well as the density index parameter, both of which have to be tuned for optimal results. We achieve a reduction in algorithm runtime, albeit at the price of statistical measure scores
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defin...
We devise coresets for kernel $k$-Means with a general kernel, and use them to obtain new, more effi...
Overlapping between clusters is a major issue in clustering. In this cluster configuration, an objec...
This work introduces a novel way to identify dense regions in a graph based on a mode-seeking cluste...
Clustering by jointly exploiting information from multiple views can yield better performance than c...
Kernel Methods are algorithms that implicitly perform a nonlinear mapping of the input data to a hig...
k'-means algorithm is a new improvement of k-means algorithm. It implements a rewarding and pen...
Abstract. This work presents a kernel method for clustering the nodes of a weighted, undirected, gra...
Recently, a variety of clustering algorithms have been proposed to handle data that is not linearly ...
Clustering is an unsupervised data exploration scenario that is of fundamental importance to pattern...
We propose a novel clustering technique based on kernel methods. We exploit the geometric properties...
Grouping the objects based on their similarities is an important common task in machine learning app...
Multiple kernel $k$-means (MKKM) aims to improve clustering performance by learning an optimal kerne...
We present an unsupervised method that selects the most relevant features using an embedded strategy...
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering, a...
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defin...
We devise coresets for kernel $k$-Means with a general kernel, and use them to obtain new, more effi...
Overlapping between clusters is a major issue in clustering. In this cluster configuration, an objec...
This work introduces a novel way to identify dense regions in a graph based on a mode-seeking cluste...
Clustering by jointly exploiting information from multiple views can yield better performance than c...
Kernel Methods are algorithms that implicitly perform a nonlinear mapping of the input data to a hig...
k'-means algorithm is a new improvement of k-means algorithm. It implements a rewarding and pen...
Abstract. This work presents a kernel method for clustering the nodes of a weighted, undirected, gra...
Recently, a variety of clustering algorithms have been proposed to handle data that is not linearly ...
Clustering is an unsupervised data exploration scenario that is of fundamental importance to pattern...
We propose a novel clustering technique based on kernel methods. We exploit the geometric properties...
Grouping the objects based on their similarities is an important common task in machine learning app...
Multiple kernel $k$-means (MKKM) aims to improve clustering performance by learning an optimal kerne...
We present an unsupervised method that selects the most relevant features using an embedded strategy...
Standard clustering methods such as K-means, Gaussian mixture models, and hierarchical clustering, a...
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defin...
We devise coresets for kernel $k$-Means with a general kernel, and use them to obtain new, more effi...
Overlapping between clusters is a major issue in clustering. In this cluster configuration, an objec...