Narrow Results

Fuzzy clustering is playing a more and more important role in text clustering because of text diversity and abundance. As the most popular fuzzy clustering algorithm, FCM, however, is rather sensitive in its initial clustering centers. This paper presents a new GA-based FCM approach (GFCM for short) to overcome this drawback, which optimizes the initial clustering centers of FCM with the global searching ability of GA. Related operators are improved to enhance clustering quality and accelerate searching process. Besides experiment results not only prove its feasibility but also reveal more effective performance, compared with previous FCM algorithm.

Clustering is primarily used to uncover the true underlying structure of a given data set. Most algorithms for clustering often depend on initial guesses of the cluster centers and assumptions made as to the number of subgroups presents in the data. In this paper, we propose a method for fuzzy clustering without initial guesses on cluster number in the data set. Our method assumes that clusters will have the normal distribution. Our method can automatically estimate the cluster number and form the clusters according to the number. In it, Genetic Algorithms (GAs) with two chromosomic coding techniques are evaluated. Graph structured coding can derive high fitness value. Linear structured can save the number of generation.