抄録
Each data on a real space is generally transformed to a point in a pattern space and analyzed in clustering. Actually, the data should be often represented not by a point but by a set because of uncertainty of the data. From such a viewpoint, some clustering algorithms which can handle the data with uncertainty have been proposed. In the conventional algorithms, uncertainty of data is estimated by the given range. However, the algorithms cannot handle the data of which the uncertainty is not regarded as range.
In this paper, we consider new optimization problems in which the uncertainty without the range and construct new clustering algorithms based on fuzzy c-means.
First, the uncertainty of the data is introduced into optimization problems using spring modulus. Next, the problems are solved and some algorithms are constructed based on the results. Finally, usefulness of the proposed algorithms is verified through numerical examples.
