IFKCN: Applying fuzzy Kohonen clustering network to interval data

The recording of interval data has become a common practice in real world applications and nowadays this kind of data is often used to describe objects. In this paper, we introduce a new fuzzy Kohonen clustering network for symbolic interval data (IFKCN). The network combine the idea of fuzzy membership values for learning rates and the algorithm is able to show superiority in processing the ambiguity and the uncertainty present in data sets. Experiments with benchmark interval data sets and an artificial interval data set for evaluating the usefulness of the proposed method were carried out.