Study On Clustering Method Based On Linguistic Information

Clustering is an unsupervised study process; the objective of cluster analysis is to group a set of objects into clusters such that objects within the same cluster have a high degree of similarity, while objects belonging to different clusters have a high degree of dissimilarity. It has been most commonly applied in the economic and the management areas, etc. If the clustering information (feature value of clustering object or similarity matrix or the feature weight) is exactly numerical (crisp) data, there are many literatures on this topic. But for many actual problems, because of the vague and the non-precise of the object's feature, the object's feature values are interval number or triangular fuzzy numbers or linguistic information even linguistic interval information forms. Therefore, with respect to the research of clustering analysis problems with linguistic information, not only in theory but also in application, there are important significances.This paper studies clustering analysis with linguistic information, with contents as follows:In chapter 1, the background, meaning, purpose and the main work of the paper are introduced; Moreover, the point of innovation and the research idea of this paper are given.In chapter 2, the methods for clustering analysis with linguistic information and its related problems are summarized.Chapter 3 gives the conception of clustering analysis and two methods of clustering, and then introduces the definitions of linguistic variable Finally, the conception and aggregation operators of 2-tuple is introduced.Chapter 4 gives the conception of interval linguistic variable, introduces the conception and aggregation operators of interval 2-tuple.And then the description of clustering problem with interval linguistic information is given. A maximal tree clustering method and a FCM clustering method base on interval linguistic 2-tuple information processing are present. Finally, two examples show the applicability of the proposed methods separately. In chapter 5, aiming at the clustering analysis problems with mixed attribute information such as real number, interval number and natural language, a new clustering analysis algorithm is proposed, which is the extension of the traditional FCM clustering method. Finally, an example is given to show the applicability of the proposed FCM clustering method.Finally, the dissertation draws a conclusion, summarizes the research fruits. On the basis of the above, some suggestions on future research are put forward.