MULTIDIMENSIONAL SEQUENCE CLUSTERING WITH ADAPTIVE ITERATIVE DYNAMIC TIME WARPING

Multimedia sequence matching is an urgent problem nowadays for the field of artificial intelligence. Despite great progress in this field of computer science, huge data arrays require near to real time processing, which significantly limits applicable methods. In this paper, the authors make an attempt of time series approach modification and enhancement with orientation for non-stationary data of different length. The processing procedure is enriched by sequences alignment with iterative dynamic time warping which implies matching two temporal data segments. Computational complexity is reduced due to Kohonen self-organizing maps applied for the purpose of clustering. Mathematical presentation is given in scalar, vector and matrix forms in order to cover all the possible use cases. An example of video sequence processing with the novel approach is provided to show its efficiency. The proposed technique can also be successfully implemented for natural language and signal processing, bioinformatics and financial data analysis.