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» Learning Pairwise Similarity for Data Clustering
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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
CVPR
2011
IEEE
14 years 1 months ago
Max-margin Clustering: Detecting Margins from Projections of Points on Lines
Given a unlabelled set of points X ∈ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We...
Raghuraman Gopalan, Jagan Sankaranarayanan
ECML
2006
Springer
15 years 1 months ago
An Adaptive Kernel Method for Semi-supervised Clustering
Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
Bojun Yan, Carlotta Domeniconi

Source Code
2231views
16 years 3 months ago
The Berkeley Segmentation Engine (BSE)
The code is a (good, in my opinion) implementation of a segmentation engine based on normalised cuts (a spectral clustering algorithm) and a pixel affinity matrix calculation algor...
Charless Fowlkes
ICPR
2010
IEEE
15 years 4 months ago
Semi-Supervised Distance Metric Learning by Quadratic Programming
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original dist...
Hakan Cevikalp