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ICML
2005
IEEE
16 years 7 months ago
Clustering through ranking on manifolds
Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-...
Markus Breitenbach, Gregory Z. Grudic
CIKM
2009
Springer
16 years 1 months ago
Fragment-based clustering ensembles
Clustering ensembles combine different clustering solutions into a single robust and stable one. Most of existing methods become highly time-consuming when the data size turns to ...
Ou Wu, Mingliang Zhu, Weiming Hu
ECML
2007
Springer
16 years 24 days ago
Spectral Clustering and Embedding with Hidden Markov Models
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
Tony Jebara, Yingbo Song, Kapil Thadani
KDD
2004
ACM
157views Data Mining» more  KDD 2004»
16 years 30 min ago
On detecting space-time clusters
Detection of space-time clusters is an important function in various domains (e.g., epidemiology and public health). The pioneering work on the spatial scan statistic is often use...
Vijay S. Iyengar
ISBRA
2007
Springer
16 years 23 days ago
Coclustering Based Parcellation of Human Brain Cortex Using Diffusion Tensor MRI
The fundamental goal of computational neuroscience is to discover anatomical features that reflect the functional organization of the brain. Investigations of the physical connect...
Cui Lin, Shiyong Lu, Danqing Wu, Jing Hua, Otto Mu...