A Machine learning algorithm based on supervised clustering and classification

Nong Ye, Xiangyang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper a novel data mining technique – Clustering and Classification Algorithm-Supervised (CCA-S)<sup>1</sup> is introduced. CCA-S supports incremental learning and non-hierarchical clustering, and is scalable for processing large data sets. CCA-S incorporates the class information in making clustering decisions, and uses the resulting clusters to classify new data records. We apply and test CCA-S on several common data sets for classification problems. The testing results show that the classification performance of CCAS is comparable to the other classification algorithms such as decision trees, artificial neural networks and discriminant analysis.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages327-334
Number of pages8
Volume2252
ISBN (Print)9783540430353
StatePublished - 2001
Event6th International Computer Science Conference on Active Media Technology, AMT 2001 - Hong Kong, China
Duration: Dec 18 2001Dec 20 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2252
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Computer Science Conference on Active Media Technology, AMT 2001
CountryChina
CityHong Kong
Period12/18/0112/20/01

Fingerprint

Classification Algorithm
Learning algorithms
Learning systems
Learning Algorithm
Machine Learning
Clustering
Incremental Learning
Network Analysis
Discriminant Analysis
Large Data Sets
Decision tree
Classification Problems
Clustering Algorithm
Artificial Neural Network
Data Mining
Discriminant analysis
Classify
Electric network analysis
Decision trees
Data mining

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Ye, N., & Li, X. (2001). A Machine learning algorithm based on supervised clustering and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2252, pp. 327-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2252). Springer Verlag.

A Machine learning algorithm based on supervised clustering and classification. / Ye, Nong; Li, Xiangyang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2252 Springer Verlag, 2001. p. 327-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2252).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ye, N & Li, X 2001, A Machine learning algorithm based on supervised clustering and classification. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2252, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2252, Springer Verlag, pp. 327-334, 6th International Computer Science Conference on Active Media Technology, AMT 2001, Hong Kong, China, 12/18/01.
Ye N, Li X. A Machine learning algorithm based on supervised clustering and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2252. Springer Verlag. 2001. p. 327-334. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Ye, Nong ; Li, Xiangyang. / A Machine learning algorithm based on supervised clustering and classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2252 Springer Verlag, 2001. pp. 327-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{fcda58af9cb741d6ac56baf4b8795d43,
title = "A Machine learning algorithm based on supervised clustering and classification",
abstract = "In this paper a novel data mining technique – Clustering and Classification Algorithm-Supervised (CCA-S)1 is introduced. CCA-S supports incremental learning and non-hierarchical clustering, and is scalable for processing large data sets. CCA-S incorporates the class information in making clustering decisions, and uses the resulting clusters to classify new data records. We apply and test CCA-S on several common data sets for classification problems. The testing results show that the classification performance of CCAS is comparable to the other classification algorithms such as decision trees, artificial neural networks and discriminant analysis.",
author = "Nong Ye and Xiangyang Li",
year = "2001",
language = "English (US)",
isbn = "9783540430353",
volume = "2252",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "327--334",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - A Machine learning algorithm based on supervised clustering and classification

AU - Ye, Nong

AU - Li, Xiangyang

PY - 2001

Y1 - 2001

N2 - In this paper a novel data mining technique – Clustering and Classification Algorithm-Supervised (CCA-S)1 is introduced. CCA-S supports incremental learning and non-hierarchical clustering, and is scalable for processing large data sets. CCA-S incorporates the class information in making clustering decisions, and uses the resulting clusters to classify new data records. We apply and test CCA-S on several common data sets for classification problems. The testing results show that the classification performance of CCAS is comparable to the other classification algorithms such as decision trees, artificial neural networks and discriminant analysis.

AB - In this paper a novel data mining technique – Clustering and Classification Algorithm-Supervised (CCA-S)1 is introduced. CCA-S supports incremental learning and non-hierarchical clustering, and is scalable for processing large data sets. CCA-S incorporates the class information in making clustering decisions, and uses the resulting clusters to classify new data records. We apply and test CCA-S on several common data sets for classification problems. The testing results show that the classification performance of CCAS is comparable to the other classification algorithms such as decision trees, artificial neural networks and discriminant analysis.

UR - http://www.scopus.com/inward/record.url?scp=84875264852&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875264852&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84875264852

SN - 9783540430353

VL - 2252

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 327

EP - 334

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -