Wu, X.; Kumar, V.; Quinlan, J. R.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.; Ng, A.; Liu, B.; Yu, P.; Zhou, Z.-H.; Steinbach, M.; Hand, D. & Steinberg, D.
(2008):
Top 10 algorithms in data mining.
In: Knowledge and Information Systems,
Ausgabe/Number: 1,
Vol. 14,
Verlag/Publisher: Springer.
Erscheinungsjahr/Year: 2008.
Seiten/Pages: 1-37.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM)
December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community.With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current andfurther research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, associationanalysis, and link mining, which are all among the most important topics in data mining research and development.
@article{wu2008wu,
author = {Wu, Xindong and Kumar, Vipin and Quinlan, J. Ross and Ghosh, Joydeep and Yang, Qiang and Motoda, Hiroshi and McLachlan, Geoffrey and Ng, Angus and Liu, Bing and Yu, Philip and Zhou, Zhi-Hua and Steinbach, Michael and Hand, David and Steinberg, Dan},
title = {Top 10 algorithms in data mining},
journal = {Knowledge and Information Systems},
publisher = {Springer},
address = {London},
year = {2008},
volume = {14},
number = {1},
pages = {1--37},
url = {http://dx.doi.org/10.1007/s10115-007-0114-2},
issn = {0219-1377},
keywords = {algorithm, ieee, icdm, top, data, mining},
abstract = {This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM)
in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community.With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current andfurther research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, associationanalysis, and link mining, which are all among the most important topics in data mining research and development.}
}
%0 = article
%A = Wu, Xindong and Kumar, Vipin and Quinlan, J. Ross and Ghosh, Joydeep and Yang, Qiang and Motoda, Hiroshi and McLachlan, Geoffrey and Ng, Angus and Liu, Bing and Yu, Philip and Zhou, Zhi-Hua and Steinbach, Michael and Hand, David and Steinberg, Dan
%C = London
%D = 2008
%I = Springer
%T = Top 10 algorithms in data mining
%U = http://dx.doi.org/10.1007/s10115-007-0114-2
Zhang, M.-L. & Zhou, Z.-H.
(2005):
A k-nearest neighbor based algorithm for multi-label classification..
In: GrC,
[Volltext]
[BibTeX][Endnote]
@inproceedings{conf/grc/ZhangZ05b,
author = {Zhang, Min-Ling and Zhou, Zhi-Hua},
title = {A k-nearest neighbor based algorithm for multi-label classification.},
editor = {Hu, Xiaohua and Liu, Qing and Skowron, Andrzej and Lin, Tsau Young and Yager, Ronald R. and Zhang, Bo},
booktitle = {GrC},
publisher = {IEEE},
year = {2005},
pages = {718-721},
url = {http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/grc05.pdf},
isbn = {0-7803-9017-2},
keywords = {label, algorithm, knn, ranking, learning, toread, multi}
}
%0 = inproceedings
%A = Zhang, Min-Ling and Zhou, Zhi-Hua
%B = GrC
%D = 2005
%I = IEEE
%T = A k-nearest neighbor based algorithm for multi-label classification.
%U = http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/grc05.pdf