Clustering Ensemble and Application in HST Dataset

Clustering ensemble is an important part of ensemble learning. It aims to study and integrate multiple clustering results from different clustering algorithms or same algorithm with different initial parameters for the same dataset. CHAMELEON is a hierarchical clustering algorithm which can discover natural clusters of different shapes and sizes as the result of its merging decision dynamically adapts to the different clustering model characterized. Inspired by the idea of CHAMELEON, the paper proposes a novel clustering ensemble model including semi-supervised method and discusses its application in fault diagnosis of high speed train (HST) running gear. The model is divided into three phases. Phase 1 is constructing a sparse graph through similarity matrix which aggregates multiple clustering results. Phase 2 is partitioning the sparse graph (vertex = object, edge weight = similarity) into a large number of relatively small sub-clusters. Phase 3 is obtaining the final clustering partition by merging these sub-clusters repeatedly. The experimental results demonstrate that our method outperforms some of state-of-the-art ensemble algorithms regarding the accuracy and stability and recognizes fault patterns of HST running gear effectively.