Object-Neighbourhood based Clustering Ensemble Method

Alqurashi, Tahani and Wang, Wenjia (2014) Object-Neighbourhood based Clustering Ensemble Method. In: The 15th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2014), 2014-09-09 - 2014-09-12.

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Abstract

Clustering is an unsupervised learning and clustering results are often inconsistent and unreliable when dierent clustering algorithms are used. In this paper we have proposed a clustering ensemble frame- work, named Object-Neighbourhood Clustering Ensemble (ONCE), to improve the consistency, reliability and quality of the clustering result. The core of the ONCE is a new consensus function that addresses the uncertain agreements between members by taking the neighbourhood relationship between object pairs into account in the similarity matrix. The experiments are carried out on 11 benchmark datasets. The results show that our ensemble method outperforms the co-association method, when the Average linkage is used. Furthermore, the results show that our ensemble method is more accurate than the baseline algorithm, and this indicates that the clustering ensemble method is more consistent and reliable than a single clustering algorithm.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	machine learning ,data mining,clustering,clsutering ensemble
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	Pure Connector
Date Deposited:	08 Sep 2014 12:48
Last Modified:	16 Sep 2021 08:58
URI:	https://ueaeprints.uea.ac.uk/id/eprint/49981
DOI:

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