Improvement Based On FCM Clustering Algorithm

Clustering is an important area of application for a variety of fields including data mining. It is also an important method of data partition or grouping. Clustering has been used in various ways including commerce, market analysis, biology, Web classification and so on. Clustering algorithms can be divided into graph-based, hierarchical, density-based, grid-based ,model-based and partitioning based algorithms.Fuzzy c -Mean (FCM) clustering algorithm is one of the widely applied algorithms in unsupervised model recognition fields. As well known, the optimal solution of FCM algorithm is obtained by minimizing the objective function. FCM clustering starts with selecting C initial clustering centers randomly(C is the number of clusters) and continue the algorithm by looping. FCM clustering is not perfect, either. Before using it, people need to know the number of clusters and good selection of initial cluster centers. If bad initial centers are picked, the objective function of FCM algorithm will not go to a minimum value.In this paper, several frequently used clustering algorithm are firstly discussed with one example. Then as the emphasis, improvement methods are introduced. In details, it concludes how to decide the number of clusters; how to get good initial clustering centers; To replace initial centers with cores of the clusters; To improve the"definition"of distance and to modify the membership value-m. Later on it is proved the improvement effect by using IRIS dataset, which is often used in clustering analysis. At last application of FCM in sea fog recognition is simply presented.