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» CURE: An Efficient Clustering Algorithm for Large Databases
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SIGMOD
1998
ACM
99views Database» more  SIGMOD 1998»
14 years 3 months ago
CURE: An Efficient Clustering Algorithm for Large Databases
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
KDD
2002
ACM
155views Data Mining» more  KDD 2002»
14 years 11 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
1996
ACM
151views Database» more  SIGMOD 1996»
14 years 3 months ago
BIRCH: An Efficient Data Clustering Method for Very Large Databases
Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters...
Tian Zhang, Raghu Ramakrishnan, Miron Livny
MLDM
2005
Springer
14 years 4 months ago
Clustering Large Dynamic Datasets Using Exemplar Points
In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. The approach is based on the dynamic representation of clus...
William Sia, Mihai M. Lazarescu
GECCO
2008
Springer
232views Optimization» more  GECCO 2008»
13 years 12 months ago
An efficient SVM-GA feature selection model for large healthcare databases
This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM an...
Rick Chow, Wei Zhong, Michael Blackmon, Richard St...