A weight-incorporated similarity-based clustering ensemble method

Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can...

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Bibliographic Details
Published inProceedings of the 11th IEEE International Conference on Networking, Sensing and Control pp. 719 - 724
Main Authors ShiYao Liu, Qi Kang, Jing An, MengChu Zhou
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2014
Subjects
Algorithm design and analysis
Clustering algorithms
clustering ensemble
Computers
data clustering
Image segmentation
Iris
Lead
similarity-based ensemble
Vehicles
weight-incorporated
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DOI10.1109/ICNSC.2014.6819714

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Summary:Clustering analysis is an important tool of data mining. The study on efficient clustering has great significance, especially in improving a clustering algorithm's adaptability and usefulness. Clustering ensemble (CE) integrates several clustering algorithms such that the clustering results can be effectively improved. This work investigates similarity-based methods and proposes a new method called weight- incorporated similarity-based clustering ensemble (WSCE). Six classic data sets are used to test single clustering algorithms, similarity-based one, and the proposed one via simulation. The results prove the validity and performance advantage of the proposed method.
DOI:10.1109/ICNSC.2014.6819714