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» Clustering with Instance-level Constraints
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PR
2007
340views more  PR 2007»
13 years 7 months ago
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
— Fuzzy c-means (FCM) algorithms with spatial constraints (FCM_S) have been proven effective for image segmentation. However, they still have the following disadvantages: 1) Alth...
Weiling Cai, Songcan Chen, Daoqiang Zhang
ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
13 years 5 months ago
D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification
: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi HP Labo...
Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yu...
ICPR
2008
IEEE
14 years 2 months ago
Constrained clustering by a novel graph-based distance transformation
In this work we present a novel method to model instance-level constraints within a clustering algorithm. Thereby, both similarity and dissimilarity constraints can be used coeval...
Kai Rothaus, Xiaoyi Jiang
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 8 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
ICCV
2005
IEEE
14 years 1 months ago
A Unifying Approach to Hard and Probabilistic Clustering
We derive the clustering problem from first principles showing that the goal of achieving a probabilistic, or ”hard”, multi class clustering result is equivalent to the algeb...
Ron Zass, Amnon Shashua