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» Watershed-Based Unsupervised Clustering
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SIGIR
2002
ACM
14 years 11 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
ICDM
2006
IEEE
139views Data Mining» more  ICDM 2006»
15 years 5 months ago
Unsupervised Clustering In Streaming Data
Tools for automatically clustering streaming data are becoming increasingly important as data acquisition technology continues to advance. In this paper we present an extension of...
Dimitris K. Tasoulis, Niall M. Adams, David J. Han...
SYNASC
2005
IEEE
170views Algorithms» more  SYNASC 2005»
15 years 5 months ago
Density Based Clustering with Crowding Differential Evolution
The aim of this work is to analyze the applicability of crowding differential evolution to unsupervised clustering. The basic idea of this approach, interpreting the clustering pr...
Daniela Zaharie
PR
2006
127views more  PR 2006»
14 years 11 months ago
Unsupervised possibilistic clustering
In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. Since the FCM memberships do not always explain the degrees of belonging for t...
Miin-Shen Yang, Kuo-Lung Wu
DEXAW
2007
IEEE
135views Database» more  DEXAW 2007»
15 years 6 months ago
Unsupervised Learning of Manifolds via Linear Approximations
In this paper, we examine the application of manifold learning to the clustering problem. The method used is Locality Preserving Projections (LPP), which is chosen because of its ...
Hassan A. Kingravi, M. Emre Celebi, Pragya P. Raja...