Exact k-Component Graph Learning for Image Clustering

K-hyperline clustering learning for sparse component analysis

A two-stage clustering-then-‘1-optimization approach has been often used for sparse component analysis (SCA). The first challenging task of this approach is to estimate the basis matrix by cluster analysis. In this paper, a robust K-hyperline clustering (K-HLC) algorithm is developed for this task. The novelty of our method is that it is not only able to implement hyperline clustering, but also...

Graph based k-means clustering

An original approach to cluster multi-component data sets is proposed that includes an estimation of the number of clusters. Using Prim’s algorithm to construct a minimal spanning tree (MST) we show that, under the assumption that the vertices are approximately distributed according to a spatial homogeneous Poisson process, the number of clusters can be accurately estimated by thresholding the ...

K - Means Clustering for Automatic Image Segmentation

Software component capture using graph clustering

We describe a simple, fast computing and easy to implement method for finding relatively good clusterings of software systems. Our method relies on the ability to compute the strength of an edge in a graph by applying a straightforward metric defined in terms of the neighborhoods of its end vertices. The metric is used to identify the weak edges of the graph, which are momentarily deleted to br...

An ensemble learning strategy for graph clustering

This paper is on a graph clustering scheme inspired by ensemble learning. In short, the idea of ensemble learning is to learn several weak classifiers and use these weak classifiers to form a strong classifier. In this contribution, we use the generic procedure of ensemble learning and determine several weak graph clusterings (with respect to the objective function). From the partition given by...