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KDD
2002
ACM
155views Data Mining» more  KDD 2002»
16 years 3 months ago
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Hichem Frigui
SIGMOD
2011
ACM
269views Database» more  SIGMOD 2011»
14 years 6 months ago
Advancing data clustering via projective clustering ensembles
Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering ensembles and projective clustering...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
ICDM
2006
IEEE
100views Data Mining» more  ICDM 2006»
15 years 9 months ago
Meta Clustering
Clustering is ill-defined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the cl...
Rich Caruana, Mohamed Farid Elhawary, Nam Nguyen, ...
BMCBI
2008
132views more  BMCBI 2008»
15 years 3 months ago
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of
Background: Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analys...
Raffaele Giancarlo, Davide Scaturro, Filippo Utro
DASFAA
2007
IEEE
178views Database» more  DASFAA 2007»
15 years 9 months ago
ClusterSheddy : Load Shedding Using Moving Clusters over Spatio-temporal Data Streams
Abstract. Moving object environments are characterized by large numbers of objects continuously sending location updates. At times, data arrival rates may spike up, causing the loa...
Rimma V. Nehme, Elke A. Rundensteiner