Abstract. Dialectometry produces aggregate distance matrices in which a distance is specified for each pair of sites. By projecting groups obtained by clustering onto geography on...
John Nerbonne, Peter Kleiweg, Wilbert Heeringa, Fr...
Given a unlabelled set of points X ∈ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We...
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
We present an algorithmic scheme for unsupervised cluster ensembles, based on randomized projections between metric spaces, by which a substantial dimensionality reduction is obtai...
Abstract. Noise significantly affects cluster quality. Conventional clustering methods hardly detect clusters in a data set containing a large amount of noise. Projected clusterin...
Jiuyong Li, Xiaodi Huang, Clinton Selke, Jianming ...