Fuzzy-Rough induced spectral ensemble clustering

Ensemble clustering helps achieve fast clustering under abundant computing resources by constructing multiple base clusterings. Compared with the standard single clustering algorithm, ensemble clustering integrates the advantages of multiple clustering algorithms and has stronger robustness and applicability. Nevertheless, most ensemble clustering algorithms treat each base clustering result equally and ignore the difference of clusters. If a cluster in a base clustering is reliable/unreliable, it should play a critical/uncritical role in the ensemble process. Fuzzy-rough sets offer a high degree of flexibility in enabling the vagueness and imprecision present in real-valued data. In this paper, a novel fuzzy-rough induced spectral ensemble approach is proposed to improve the performance of clustering. Specifically, the significance of clusters is differentiated, and the unacceptable degree and reliability of clusters formed in base clustering are induced based on fuzzyrough lower approximation. Based on defined cluster reliability, a new co-association matrix is generated to enhance the effect of diverse base clusterings. Finally, a novel consensus spectral function is defined by the constructed adjacency matrix, which can lead to significantly better results. Experimental results confirm that the proposed approach works effectively and outperforms many state-of-the-art ensemble clustering algorithms and base clustering, which illustrates the superiority of the novel algorithm.

Automated summary

Ensemble clustering involves constructing multiple base clusterings to achieve fast clustering under abundant computing resources. By integrating multiple clustering algorithms, ensemble clustering has stronger robustness and applicability compared to a single clustering algorithm. However, most ensemble clustering algorithms treat each base clustering result equally and overlook the differences between clusters. This paper proposes a novel fuzzy-rough induced spectral ensemble approach to enhance clustering performance. The proposed approach differentiates the significance of clusters and induces the unacceptable degree and reliability of clusters formed in base clustering based on fuzzy-rough lower approximation. Experimental results demonstrate the effectiveness of the proposed approach, which outperforms state-of-the-art ensemble clustering algorithms and base clustering, highlighting the superiority of this novel algorithm.