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» A Novel Framework for Discovering Robust Cluster Results
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158
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KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 6 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
161
Voted
WWW
2007
ACM
16 years 6 months ago
U-REST: an unsupervised record extraction system
In this paper, we describe a system that can extract record structures from web pages with no direct human supervision. Records are commonly occurring HTML-embedded data tuples th...
Yuan Kui Shen, David R. Karger
ECCV
2006
Springer
15 years 9 months ago
Learning Semantic Scene Models by Trajectory Analysis
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...
Xiaogang Wang, Kinh Tieu, Eric Grimson
195
Voted
CVPR
2008
IEEE
16 years 8 months ago
Learning human actions via information maximization
In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a ba...
Jingen Liu, Mubarak Shah
CVPR
2011
IEEE
15 years 2 months ago
From Partial Shape Matching through Local Deformation to Robust Global Shape Similarity for Object Detection
In this paper, we propose a novel framework for contour based object detection. Compared to previous work, our contribution is three-fold. 1) A novel shape matching scheme suitabl...
Tianyang Ma, LonginJan Latecki