The Improvement On The Fuzzy C-means Algorithm

With the rapid development of computer technology and database technology, Data Mining has been widely applied. As is known to all, Cluster analysis is one of the active research subject in Data Mining. Placing great study on it has not only important theoretical value, but also great engineering value. Clustering is to group data objects into multiple clusters, in the same cluster objects in a high similarity, while objects in different clusters in the greater dissimilarity. Presently, Cluster analysis has been widely used in pattern recognition, data mining, image processing, machine learning and many other fields.Compared with traditional Clustering algorithms, fuzzy clustering has established the sample for the class of uncertain descriptions, and being able to reflect the real world more objectively, and gradually becomes the mainstream of Cluster analysis. Among the lots of fuzzy clustering algorithms, fuzzy c-means clustering algorithm(FCM) is currently the most popular algorithm. However, the algorithm has the shortcoming of being sensitive to the initial cluster centers, needing to know the number of clusters in advance and slow rate of convergence.This paper introduces a variety of traditional clustering algorithms detailedly and its key technologies, and compares the performance of several commonly used algorithms. Both the hard c-means clustering algorithm and the fuzzy-means clustering algorithm are described respectively, and the process of the fuzzy c-means clustering algorithm is researched and analyzed detailedly. The fuzzy c-means clustering algorithm is improved by applying the idea of phased clustering, and the following phases are devided: first, the number of clustering and the initial clustering center is automatically generated, and better initial parameters can be gained; second, the speed of convergence is quicken and the clustring effect is improved by modifying membership matrix and clustering center, so an improved fuzzy c-means clustering algorithm is presented. Finally, by using Yunxi soil erosion over the years as a data, C#.net 2005 and SQL Server 2005 as a tool ,and the improved fuzzy c-means algorithm, the classification of soil erosion is achieved and better results are gained.