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ICPR
2008
IEEE
14 years 12 days ago
A novel validity measure for clusters of arbitrary shapes and densities
Several validity indices have been designed to evaluate solutions obtained by clustering algorithms. Traditional indices are generally designed to evaluate center-based clustering...
Noha A. Yousri, Mohamed S. Kamel, Mohamed A. Ismai...
ICDM
2003
IEEE
184views Data Mining» more  ICDM 2003»
13 years 11 months ago
Analyzing High-Dimensional Data by Subspace Validity
We are proposing a novel method that makes it possible to analyze high dimensional data with arbitrary shaped projected clusters and high noise levels. At the core of our method l...
Amihood Amir, Reuven Kashi, Nathan S. Netanyahu, D...
ICDM
2010
IEEE
232views Data Mining» more  ICDM 2010»
13 years 3 months ago
gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration
Community detection is an important task for mining the structure and function of complex networks. Many pervious approaches are difficult to detect communities with arbitrary size...
Heli Sun, Jianbin Huang, Jiawei Han, Hongbo Deng, ...
ICPR
2008
IEEE
14 years 12 days ago
A uniformity criterion and algorithm for data clustering
We propose a novel multivariate uniformity criterion for testing uniformity of point density in an arbitrary dimensional point pattern . An unsupervised, nonparametric data cluste...
Sanketh Shetty, Narendra Ahuja
ECCV
2002
Springer
14 years 7 months ago
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
We present a variational integration of nonlinear shape statistics into a Mumford?Shah based segmentation process. The nonlinear statistics are derived from a set of training silho...
Christoph Schnörr, Daniel Cremers, Timo Kohlb...