Name
Abstract | ||
|---|---|---|
A clustering ensemble aims to combine multiple clustering models to produce a better result than that of the individual clustering algorithms in terms of consistency and quality. In this paper, we propose a clustering ensemble algorithm with a novel consensus function named Adaptive Clustering Ensemble. It employs two similarity measures, cluster similarity and a newly defined membership similarity, and works adaptively through three stages. The first stage is to transform the initial clusters into a binary representation, and the second is to aggregate the initial clusters that are most similar based on the cluster similarity measure between clusters. This iterates itself adaptively until the intended candidate clusters are produced. The third stage is to further refine the clusters by dealing with uncertain objects to produce an improved final clustering result with the desired number of clusters. Our proposed method is tested on various real-world benchmark datasets and its performance is compared with other state-of-the-art clustering ensemble methods, including the Co-association method and the Meta-Clustering Algorithm. The experimental results indicate that on average our method is more accurate and more efficient. |
Year | DOI | Keywords |
|---|---|---|
2019 | 10.1007/s13042-017-0756-7 | Clustering ensemble, K-means, Similarity measurement, Machine learning, Data mining |
DocType | Volume | Issue |
Journal | 10 | 6 |
ISSN | Citations | PageRank |
1868-808X | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tahani Alqurashi | 1 | 2 | 1.71 |
| Wenjia Wang | 2 | 57 | 9.12 |