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ICML
2001
IEEE
16 years 7 months ago
Constrained K-means Clustering with Background Knowledge
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data in...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan ...
HAIS
2009
Springer
15 years 11 months ago
Multiobjective Evolutionary Clustering Approach to Security Vulnerability Assesments
Network vulnerability assessments collect large amounts of data to be further analyzed by security experts. Data mining and, particularly, unsupervised learning can help experts an...
Guiomar Corral, A. Garcia-Piquer, Albert Orriols-P...
DILS
2008
Springer
15 years 8 months ago
Semi Supervised Spectral Clustering for Regulatory Module Discovery
We propose a novel semi-supervised clustering method for the task of gene regulatory module discovery. The technique uses data on dna binding as prior knowledge to guide the proces...
Alok Mishra, Duncan Gillies
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
15 years 8 months ago
Are approximation algorithms for consensus clustering worthwhile?
Consensus clustering has emerged as one of the principal clustering problems in the data mining community. In recent years the theoretical computer science community has generated...
Michael Bertolacci, Anthony Wirth
PR
2007
107views more  PR 2007»
15 years 6 months ago
Newtonian clustering: An approach based on molecular dynamics and global optimization
Given a data set, a dynamical procedure is applied to the data points in order to shrink and separate, possibly overlapping clusters. Namely, Newton’s equations of motion are em...
Konstantinos Blekas, Isaac E. Lagaris