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» A Clustering Algorithm Incorporating Density and Direction
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IEEEVAST
2010
13 years 2 months ago
Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each ob...
Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott Trager...
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 8 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
ICASSP
2011
IEEE
12 years 11 months ago
Clustering and suppression of transient noise in speech signals using diffusion maps
Recently we have presented a novel approach for transient noise reduction that relies on non-local (NL) filtering. In this paper, we modify and extend our approach to support clu...
Ronen Talmon, Israel Cohen, Sharon Gannot
BMCBI
2007
160views more  BMCBI 2007»
13 years 7 months ago
Identifying protein complexes directly from high-throughput TAP data with Markov random fields
Background: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms ...
Wasinee Rungsarityotin, Roland Krause, Arno Sch&ou...
ICTAI
2005
IEEE
14 years 1 months ago
Cellular Ants: Combining Ant-Based Clustering with Cellular Automata
This paper proposes a novel data clustering algorithm, coined ‘cellular ants’, which combines principles of cellular automata and ant colony optimization algorithms to group s...
Andrew Vande Moere, Justin James Clayden