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» An Abstract Weighting Framework for Clustering Algorithms
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SDM
2004
SIAM
189views Data Mining» more  SDM 2004»
11 years 2 months ago
An Abstract Weighting Framework for Clustering Algorithms
act Weighting Framework for Clustering Algorithms Richard Nock Frank Nielsen Recent works in unsupervised learning have emphasized the need to understand a new trend in algorithmi...
Richard Nock, Frank Nielsen
HICSS
2002
IEEE
212views Biometrics» more  HICSS 2002»
11 years 6 months ago
Connectivity-Based k-Hop Clustering in Wireless Networks
Abstract. In this paper we describe several new clustering algorithms for nodes in a mobile ad hoc network. The main contribution is to generalize the cluster deļ¬nition and forma...
Geng Chen, Fabian Garcia Nocetti, Julio Solano-Gon...
FSTTCS
2001
Springer
11 years 5 months ago
On Clustering Using Random Walks
Abstract. We propose a novel approach to clustering, based on deterministic analysis of random walks on the weighted graph associated with the clustering problem. The method is cen...
David Harel, Yehuda Koren
CIKM
2006
Springer
11 years 3 months ago
Efficiently clustering transactional data with weighted coverage density
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
Hua Yan, Keke Chen, Ling Liu
ICDM
2005
IEEE
151views Data Mining» more  ICDM 2005»
11 years 7 months 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...
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