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KDD
2009
ACM
611views Data Mining» more  KDD 2009»
15 years 10 months ago
Fast approximate spectral clustering
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Donghui Yan, Ling Huang, Michael I. Jordan
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
15 years 10 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
MICCAI
2004
Springer
15 years 11 months ago
Clustering Fiber Traces Using Normalized Cuts
In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to cre...
Anders Brun, Hans Knutsson, Hae-Jeong Park, Martha...
DMSN
2006
ACM
15 years 4 months ago
Intelligent system monitoring on large clusters
Modern data centers have a large number of components that must be monitored, including servers, switches/routers, and environmental control systems. This paper describes InteMon,...
Jimeng Sun, Evan Hoke, John D. Strunk, Gregory R. ...
CVPR
2011
IEEE
14 years 6 months ago
Iterative Quantization: A Procrustean Approach to Learning Binary Codes
This paper addresses the problem of learning similaritypreserving binary codes for efficient retrieval in large-scale image collections. We propose a simple and efficient altern...
Yunchao Gong, Svetlana Lazebnik