Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
The paper presents new clustering algorithms which are based on fuzzy c-means. The algorithms can treat data with tolerance defined as hyper-rectangle. At first, the tolerance is introduced into optimization problems of clustering. This is generalization of calculation errors or missing values. Next, algorithms are constructed based on the results which are obtained by solving the optimization problems. Finally, usefulness of the proposed algorithms is verified through numerical examples.
