A correlation-based bi-partition hierarchical clustering method for mode identification of multimode processes

Clustering is a popular method to deal with the problem for mode identification of multimode processes. Unlike traditional distance-based clustering methods, in this paper, a new correlation-based bi-partition hierarchical clustering (CBHC) method is proposed, which classifies the observations according to their correlation relationships rather than their distances. Motivated by an existing correlation-based mode identification method, a modified similarity matrix is first given by introducing normalization and sparseness into that of the existing method, then a bi-partition hierarchical clustering is used to further classify the observations. The proposed method can remove two strict assumptions required by the existing correlation-based mode identification method, i.e. the orthogonal assumption and the assumption that the number of modes should be known in advance. The merits of the proposed method are proved through two numerical examples.