R. Xu, and I. Wunsch. Neural Networks, IEEE Transactions on, 16 (3):
645--678(2005)
Abstract
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The iversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion.
We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
%0 Journal Article
%1 Rui05SurveyClustering
%A Xu, Rui
%A Wunsch, II
%D 2005
%J Neural Networks, IEEE Transactions on
%K 2005 algorithms clustering d4.1 datamining exploratory-data-analysis partitioning statistics survey tagora
%N 3
%P 645--678
%T Survey of clustering algorithms
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&isnumber=30822&arnumber=1427769
%V 16
%X Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The iversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion.
We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
@article{Rui05SurveyClustering,
abstract = {Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The iversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion.
We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.},
added-at = {2007-03-26T13:57:05.000+0200},
author = {Xu, Rui and Wunsch, II},
biburl = {https://www.bibsonomy.org/bibtex/292c03ba02a41f95ae315273939c8daa5/andreab},
interhash = {7bd8c3f3c7ea707f110d76123e0d097c},
intrahash = {92c03ba02a41f95ae315273939c8daa5},
issn = {1045-9227},
journal = {Neural Networks, IEEE Transactions on},
keywords = {2005 algorithms clustering d4.1 datamining exploratory-data-analysis partitioning statistics survey tagora},
number = 3,
owner = {mgrani},
pages = {645--678},
timestamp = {2007-03-26T13:57:05.000+0200},
title = {Survey of clustering algorithms},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&isnumber=30822&arnumber=1427769},
volume = 16,
year = 2005
}