We present a way to add user's background knowledge to formal concept analysis. The type of background knowledge we deal with relates to relative importance of attributes in ...
This paper aims to introduce the robustness against noise into the spectral clustering algorithm. First, we propose a warping model to map the data into a new space on the basis o...
Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size inc...
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belk...
Abstract. In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of mo...