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.
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.
| Original language | English |
|---|---|
| Publication status | Published - 10 Sept 2014 |
| Event | 15th International Conference on Intelligent Data Engineering and Automated Learning - Salamanca, Spain Duration: 9 Sept 2014 → 12 Sept 2014 |
Conference
| Conference | 15th International Conference on Intelligent Data Engineering and Automated Learning |
|---|---|
| Abbreviated title | IDEAL 2014 |
| Country/Territory | Spain |
| City | Salamanca |
| Period | 9/09/14 → 12/09/14 |
Keywords
- Machine Learning
- Data mining
- Clustering
- Clsutering Ensemble
Profiles
-
Wenjia Wang
- School of Computing Sciences - Professor of Artificial Intelligence
- Data Science and AI - Member
Person: Research Group Member, Academic, Teaching & Research