Abstract
In this thesis, we proposed a method to solve a general clustering problem of which the data is fuzzy. There are two major parts in this thesis: one is model-development and the other is a practical application. Regarding the model-development, we extended the Bi-Objective Fuzzy C-means method to the one that can classify fuzzy data by the interval of any h-cut. When we use the proposed method, we not only have the most homogeneous classification(when h=l), but also have different clusterings from different h values. As for the practical applications, we have to classify ten potential services provided by Broad ban information network into three clusters, so that these services in the same cluster can be developed simultaneously. Besides, if we have known the actual cluster in which all the crisp data could belong to, then we can fuzzify these crisp data. If the results of classifying these fuzzified data at certain h-level is the same as that of the original crisp data, then once a collected datum falls in this h-level, we can identify its belonged cluster.