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 language | English |
|---|---|
| Title of host publication | 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain Duration: Jul 18 2010 → Jul 23 2010 |
Other
| Other | 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 7/18/10 → 7/23/10 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
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 proceeding › Conference contribution
}
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 -