A Novel Cluster Ensemble Based on a Single Clustering Algorithm

• Autorzy: Khan Tahseen , Tian Wenhong , Kadhim Mustafa R. , Buyya Rajkumar

Identyfikatory

DOI

10.15439/2021F28

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

In recent years, several cluster ensemble methods have been developed, but they still have some limitations. They often use different clustering algorithms in both stages of the clustering ensemble method, such as the ensemble generation step and the consensus function, resulting in a compatibility issues. To deal with it, we propose a novel cluster ensemble method based on an identical clustering algorithm (CEI). Experiments on real-world datasets from various sources show that CEI improves accuracy by 5% on average compared to state-of-the-art cluster ensemble methods and by 55.54% compared to AP while consuming 44.60% less execution time.

Słowa kluczowe

EN

learning; artificial intelligence; pattern clustering

PL

uczenie się; sztuczna inteligencja; algorytm grupowania

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 127--135
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wz.

Twórcy

autor

Khan Tahseen

• School of Information and Software Engineering, University of Electronic Science and Technology of China, China

autor

Tian Wenhong

• School of Information and Software Engineering, University of Electronic Science and Technology of China, China

autor

Kadhim Mustafa R.

• School of Information and Software Engineering, University of Electronic Science and Technology of China, China

autor

Buyya Rajkumar

• School of Information and Software Engineering, University of Electronic Science and Technology of China, China
• Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems, The University of Melbourne, Australia

Bibliografia

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Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Short Paper