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» A Genetic Algorithm for Clustering on Very Large Data Sets
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DATAMINE
2006
166views more  DATAMINE 2006»
13 years 5 months ago
Accelerated EM-based clustering of large data sets
Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms l...
Jakob J. Verbeek, Jan Nunnink, Nikos A. Vlassis
AI
2005
Springer
13 years 10 months ago
A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets
Spatial clustering is an active research area in spatial data mining with various methods reported. In this paper, we compare two density-based methods, DBSCAN and DBRS. First, we ...
Xin Wang, Howard J. Hamilton
ADC
2006
Springer
120views Database» more  ADC 2006»
13 years 11 months ago
Approximate data mining in very large relational data
In this paper we discuss eNERF, an extended version of non-Euclidean relational fuzzy c-means (NERFCM) for approximate clustering in very large (unloadable) relational data. The e...
James C. Bezdek, Richard J. Hathaway, Christopher ...
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
14 years 5 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han
SIGMOD
1996
ACM
151views Database» more  SIGMOD 1996»
13 years 9 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