A clustering ensemble method for clustering mixed data

Jamil Al-Shaqsi, Wenjia Wang

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

6 Citations (Scopus)

Abstract

This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output. Our ensemble is constructed with our proposed clustering algorithm as a core modelling method that is used to generate a series of clustering results with different conditions for a given dataset. Then, a decision aggregation mechanism such as voting is employed to find a combined partition of the different clusters. The voting mechanism considered only experimental results that produce intra-similarity value higher than the average intra-similarity value for a particular interval. The aim of this procedure is to find a clustering result that minimizes the number of disagreements between different clustering results. Our ensemble method has been tested on 11 benchmark datasets and compared with some individual methods including TwoStep, k-means, squeezer, k-prototype and some ensemble based methods including k-ANMI, ccdByEnsemble, SIPR, and SICM. The experimental results showed its strengths over the compared clustering algorithms.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
Publication statusPublished - 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: Jul 18 2010Jul 23 2010

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period7/18/107/23/10

Fingerprint

Clustering algorithms
Fusion reactions
Agglomeration

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Al-Shaqsi, J., & Wang, W. (2010). A clustering ensemble method for clustering mixed data. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 [5596684] https://doi.org/10.1109/IJCNN.2010.5596684

A clustering ensemble method for clustering mixed data. / Al-Shaqsi, Jamil; Wang, Wenjia.

2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010. 5596684.

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

Al-Shaqsi, J & Wang, W 2010, A clustering ensemble method for clustering mixed data. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010., 5596684, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, 7/18/10. https://doi.org/10.1109/IJCNN.2010.5596684
Al-Shaqsi J, Wang W. A clustering ensemble method for clustering mixed data. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010. 5596684 https://doi.org/10.1109/IJCNN.2010.5596684
Al-Shaqsi, Jamil ; Wang, Wenjia. / A clustering ensemble method for clustering mixed data. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010.
@inproceedings{68170a82225e42edbca2e7063a9fe81c,
title = "A clustering ensemble method for clustering mixed data",
abstract = "This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output. Our ensemble is constructed with our proposed clustering algorithm as a core modelling method that is used to generate a series of clustering results with different conditions for a given dataset. Then, a decision aggregation mechanism such as voting is employed to find a combined partition of the different clusters. The voting mechanism considered only experimental results that produce intra-similarity value higher than the average intra-similarity value for a particular interval. The aim of this procedure is to find a clustering result that minimizes the number of disagreements between different clustering results. Our ensemble method has been tested on 11 benchmark datasets and compared with some individual methods including TwoStep, k-means, squeezer, k-prototype and some ensemble based methods including k-ANMI, ccdByEnsemble, SIPR, and SICM. The experimental results showed its strengths over the compared clustering algorithms.",
author = "Jamil Al-Shaqsi and Wenjia Wang",
year = "2010",
doi = "10.1109/IJCNN.2010.5596684",
language = "English",
isbn = "9781424469178",
booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010",

}

TY - GEN

T1 - A clustering ensemble method for clustering mixed data

AU - Al-Shaqsi, Jamil

AU - Wang, Wenjia

PY - 2010

Y1 - 2010

N2 - This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output. Our ensemble is constructed with our proposed clustering algorithm as a core modelling method that is used to generate a series of clustering results with different conditions for a given dataset. Then, a decision aggregation mechanism such as voting is employed to find a combined partition of the different clusters. The voting mechanism considered only experimental results that produce intra-similarity value higher than the average intra-similarity value for a particular interval. The aim of this procedure is to find a clustering result that minimizes the number of disagreements between different clustering results. Our ensemble method has been tested on 11 benchmark datasets and compared with some individual methods including TwoStep, k-means, squeezer, k-prototype and some ensemble based methods including k-ANMI, ccdByEnsemble, SIPR, and SICM. The experimental results showed its strengths over the compared clustering algorithms.

AB - This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output. Our ensemble is constructed with our proposed clustering algorithm as a core modelling method that is used to generate a series of clustering results with different conditions for a given dataset. Then, a decision aggregation mechanism such as voting is employed to find a combined partition of the different clusters. The voting mechanism considered only experimental results that produce intra-similarity value higher than the average intra-similarity value for a particular interval. The aim of this procedure is to find a clustering result that minimizes the number of disagreements between different clustering results. Our ensemble method has been tested on 11 benchmark datasets and compared with some individual methods including TwoStep, k-means, squeezer, k-prototype and some ensemble based methods including k-ANMI, ccdByEnsemble, SIPR, and SICM. The experimental results showed its strengths over the compared clustering algorithms.

UR - http://www.scopus.com/inward/record.url?scp=79959461219&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79959461219&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2010.5596684

DO - 10.1109/IJCNN.2010.5596684

M3 - Conference contribution

AN - SCOPUS:79959461219

SN - 9781424469178

BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

ER -