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 analysis use only these feature vectors and do not take into account changes over time. They can therefore be called static. But often a "dynamic" approach, which utilizes the feature changes over time, seems to be more appropriate (e.g. supervision of patients in medical care, state-dependent maintenance of machines, classification of shares). In this paper, different criteria for structuring the field of "dynamic data analysis (DDA)" are proposed and one of the relevant approaches is investigated in more detail. This approach considers possible ways to handle dynamics within static methods for data analysis. In doing this, different types of similarity measures for trajectories are defined, which can be used to modify static methods for data analysis. One of the proposed similarity measures has been integrated into the fuzzy c-means. An application example is used to demonstrate the applicability of the modified fuzzy c-means. (C) 1999 Elsevier Science B.V. All rights reserved.
dynamic data analysis cluster analysis pattern recognition acoustic quality control