Partial Clustering Ensemble

doi:10.1109/TKDE.2023.3321913

Partial Clustering Ensemble

2024-05-01

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DOI：10.1109/TKDE.2023.3321913

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Abstract

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Clustering ensemble often provides robust and stable results without accessing original features of data, and thus has been widely studied. The conventional clustering ensemble methods often take the full multiple base partitions as inputs and provide a consensus clustering result. However, in many real-world applications, full base partitions are hard to obtain because some data may be missing in some base partitions. To tackle this problem, in this paper, we propose a novel partial clustering ensemble method, which takes the partial multiple base partitions as inputs. In this method, we simultaneously fill the missing values in the base partitions and ensemble them by fully considering the consensus and diversity. Moreover, to address the unreliability issue in the partial data scenario, we seamlessly plug it into a self-paced learning framework. The extensive experiments on benchmark data sets demonstrate the effectiveness and efficiency of the proposed method when handling incomplete data.

Keywords:

Ensemble learning

Reliability

Clustering methods

Task analysis

Data privacy

Computer science

Urban areas

Clustering ensemble

incomplete data

Partial Clustering Ensemble

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