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IDA
2007
Springer
14 years 4 months ago
DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assign...
Alexander Hinneburg, Hans-Henning Gabriel
ISNN
2004
Springer
14 years 3 months ago
Unsupervised Learning for Hierarchical Clustering Using Statistical Information
This paper proposes a novel hierarchical clustering method that can classify given data without specified knowledge of the number of classes. In this method, at each node of a hie...
Masaru Okamoto, Nan Bu, Toshio Tsuji
CSDA
2007
142views more  CSDA 2007»
13 years 10 months ago
DIVCLUS-T: A monothetic divisive hierarchical clustering method
DIVCLUS-T is a divisive hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. It i...
Marie Chavent, Yves Lechevallier, Olivier Briant
SIGMOD
2002
ACM
246views Database» more  SIGMOD 2002»
14 years 10 months ago
Hierarchical subspace sampling: a unified framework for high dimensional data reduction, selectivity estimation and nearest neig
With the increased abilities for automated data collection made possible by modern technology, the typical sizes of data collections have continued to grow in recent years. In suc...
Charu C. Aggarwal
ICDM
2009
IEEE
153views Data Mining» more  ICDM 2009»
13 years 8 months ago
A New Clustering Algorithm Based on Regions of Influence with Self-Detection of the Best Number of Clusters
Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-b...
Fabrice Muhlenbach, Stéphane Lallich