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A multi-level consensus function clustering ensemble
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DOI：10.1007/s00500-021-06092-7.png)
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In order to improve the performance of a clustering on a data set, a number of primary partitions are generated and stored in an ensemble and their aggregated consensus partition is used as their clustering. It is widely accepted that the consensus partition outperforms the primary partitions. In this paper, an ensemble clustering method called multi-level consensus clustering (MLCC) is proposed. To construct the MLCC, a cluster-cluster similarity matrix which is achieved by an innovative similarity metric is first generated. The mentioned cluster-cluster similarity matrix is based on a multi-level similarity metric. In fact, it can be computed in a new defined multi-level space. Then, a point-point similarity matrix which is boosted using the mentioned cluster-cluster similarity matrix is generated. The new consensus function applies an average linkage hierarchical clusterer algorithm on the mentioned point-point similarity matrix to make consensus partition. MLCC is better than traditional clustering ensembles and simple versions of clustering ensembles on traditional cluster-cluster similarity matrix. Its computational cost is not very bad too. Accuracy and robustness of the proposed method are compared with those of the modern clustering algorithms through the experimental tests. Also, time analysis is presented in the experimental results.
Keyword:
Consensus partition
Multi-level similarity metric
Ensemble learning
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
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