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.
Original language | English (US) |
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Title of host publication | Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 |
Publisher | IEEE Computer Society |
Pages | 719-724 |
Number of pages | 6 |
ISBN (Print) | 9781479931064 |
DOIs | |
State | Published - Jan 1 2014 |
Event | 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 - Miami, FL, United States Duration: Apr 7 2014 → Apr 9 2014 |
Other
Other | 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014 |
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Country/Territory | United States |
City | Miami, FL |
Period | 4/7/14 → 4/9/14 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Control and Systems Engineering