A novel clustering method for complex signals and feature extraction based on advanced information-based dissimilarity measure.

• Generalization of binary sequences to multivariate improves data representation. • The weighted-probability of pattern occurrence is constructed based on complexity. • The MDS based on the novel dissimilarity measure is constructed. • The MDS creates better clustering results when compared with other methods. • The method has an acceptable running time and possesses robustness to noisy data. In this paper, a new dissimilarity measure for more accurate feature extraction and clustering is put forward. The method is proposed from the perspective of the weighted-probability distribution of dispersion patterns and their rank order statistics, where an effective quantization procedure is provided and the loss of information can be reduced. The proposed dissimilarity is applied in the multidimensional scaling (MDS) method to investigate simulated and reality-based signals. The comparative experiment shows that the clustering results of the proposed technique are clearer and more appropriate. State-of-the-art techniques and conventional methods are both included in the comparative experiments. In particular, for the heartbeat signals, it is discovered that the distribution of the weighted-probabilities of the dispersion patterns can discriminate subjects with different physiological conditions and exhibit visible changes of the subject's dynamical features when aging and disease attacks are taken place, which can be regarded as a microscopic insight of the dynamical mechanisms. [ABSTRACT FROM AUTHOR]