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 |
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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 |
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Country | Spain |
City | Barcelona |
Period | 7/18/10 → 7/23/10 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence