Abstract. There exist numerous algorithms that cluster data-points from largescale genomic experiments such as sequencing, gene-expression and proteomics. Such algorithms may emplo...
We present a novel clustering method using HMM parameter space and eigenvector decomposition. Unlike the existing methods, our algorithm can cluster both constant and variable leng...
When time-triggered (TT) systems are to be deployed for large embedded real-time (RT) control systems in cars and airplanes, one way to overcome bandwidth limitations and achieve ...
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
—In this paper we propose a clustering and routing scheme for wireless sensor networks based on a self-organizing approach. The aim of this approach is for nodes to perform an in...