Name
Abstract | ||
|---|---|---|
Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results.
It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient
and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this
paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises
an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different
data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering
results than the original CC algorithm.
|
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03915-7_30 | Intelligent Data Analysis |
Keywords | Field | DocType |
accurate clustering result,individual clustering algorithm,multi-optimisation consensus clustering,original cc algorithm,robust method,existing ensemble,simulated annealing,multi-optimisation framework,consensus clustering,multi-optimisation.,ensemble clustering,advanced consensus clustering algorithm | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Consensus clustering,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Clustering high-dimensional data,Pattern recognition,Correlation clustering,Machine learning | Conference |
Volume | ISSN | Citations |
5772 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Li | 1 | 2 | 0.71 |
| Stephen Swift | 2 | 427 | 31.32 |
| Xiaohui Liu | 3 | 5042 | 269.99 |