The purpose of text clustering in information retrieval is to discover groups of semantically related documents. Accurate and comprehensible cluster descriptions (labels) let the ...
We address the problem of fast, large scale sketch-based image retrieval, searching in a database of over one million images. We show that current retrieval methods do not scale w...
Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur,...
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
In this paper, we propose a new approach to automatically clustering e-commerce search engines (ESEs) on the Web such that ESEs in the same cluster sell similar products. This all...
This paper addresses the challenging problem of similarity search over widely distributed ultra-high dimensional data. Such an application is retrieval of the top-k most similar d...