@inproceedings{079003d5d1a646c7af0f0d3cc4864e55,
title = "A weight-incorporated similarity-based clustering ensemble method",
abstract = "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.",
keywords = "clustering ensemble, data clustering, similarity-based ensemble, weight-incorporated",
author = "Liu, {Shi Yao} and Qi Kang and Jing An and Zhou, {Meng Chu}",
year = "2014",
doi = "10.1109/ICNSC.2014.6819714",
language = "English (US)",
isbn = "9781479931064",
series = "Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014",
publisher = "IEEE Computer Society",
pages = "719--724",
booktitle = "Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014",
address = "United States",
note = "11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 ; Conference date: 07-04-2014 Through 09-04-2014",
}