Dynamic fuzzy data analysis based on similarity between functions

In data analysis, objects are usually represented by feature vectors, each describing a state of an object at a point of time. Mon methods for data an alysis use only these feature vectors and do not take into account changes over time. They can therefore be called static. But often a "dynamic" appro ach, which utilizes the feature changes over time, seems to be more appropr iate (e.g. supervision of patients in medical care, state-dependent mainten ance of machines, classification of shares). In this paper, different crite ria for structuring the field of "dynamic data analysis (DDA)" are proposed and one of the relevant approaches is investigated in more detail. This ap proach considers possible ways to handle dynamics within static methods for data analysis. In doing this, different types of similarity measures for t rajectories are defined, which can be used to modify static methods for dat a analysis. One of the proposed similarity measures has been integrated int o the fuzzy c-means. An application example is used to demonstrate the appl icability of the modified fuzzy c-means. (C) 1999 Elsevier Science B.V. All rights reserved.