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PAMI
2008
161views more  PAMI 2008»
15 years 6 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 7 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
ICDM
2005
IEEE
151views Data Mining» more  ICDM 2005»
16 years 3 days ago
A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
GFKL
2004
Springer
135views Data Mining» more  GFKL 2004»
15 years 12 months ago
KMC/EDAM: A New Approach for the Visualization of K-Means Clustering Results
In this work we introduce a method for classification and visualization. In contrast to simultaneous methods like e.g. Kohonen SOM this new approach, called KMC/EDAM, runs through...
Nils Raabe, Karsten Luebke, Claus Weihs
ICCV
2009
IEEE
1821views Computer Vision» more  ICCV 2009»
16 years 11 months ago
Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering
We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local ...
Minsu Cho (Seoul National University), Jungmin Lee...