We propose a method based on sparse representation
(SR) to cluster data drawn from multiple low-dimensional
linear or affine subspaces embedded in a high-dimensional
space. Our ...
—Identification of the correct number of clusters and the corresponding partitioning are two important considerations in clustering. In this paper, a newly developed point symme...
We present a coherent framework for data clustering. Starting with a Hopfield network, we show the solutions for several well-motivated clustering objective functions are principa...
Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings ...
Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) R...
This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel...