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» A Novel Framework for Discovering Robust Cluster Results
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KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 9 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
WWW
2007
ACM
14 years 9 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
14 years 14 days 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
CVPR
2008
IEEE
14 years 10 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
13 years 5 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