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As one of the most popular clustering algorithms, k-means is easily influenced by initial points and the number of clusters, besides, the iterative class center calculated by the mean of all points in a cluster is one of the reasons influencing clustering performance. Representational initial points are selected in this paper according to the decision graph composed by local density and distance of each point. Then we propose an improved k-means text clustering algorithm, the iterative class center of the improved algorithm is composed by subject feature vector which can avoid the influence caused by noises. Experiments show that the initial points are selected successfully and the clustering results improve 3%, 5%, 2% and 7% respectively than traditional k-means clustering algorithm on four experimental corpuses of Fudan and Sougou.

Fuzzy C-means (FCM) algorithm is a fuzzy clustering algorithm based on objective function compared with typical “hard clustering” such as k-means algorithm. FCM algorithm calculates the membership degree of each sample to all classes and obtain more reliable and accurate classification results. However, in the process of clustering, FCM algorithm needs to determine the number of clusters manually, and is sensitive to the initial clustering center. It is easy to generate problems such as multiple clustering iterations, slow convergence speed and local optimal solution. To address those problems, we propose to combine the FCM algorithm and DPC (Clustering by fast search and find of density peaks) algorithm. First, DPC algorithm is used to automatically select the center and number of clusters, and then FCM algorithm is used to realize clustering. The comparison experiments show that the improved FCM algorithm has a faster convergence speed and higher accuracy.

Sports Illustrated senior writer David Epstein discusses the popularity of the magazine's swimsuit issue, the odds of the New York Yankees making the World Series and the latest on Oscar Pistorius' murder accusation. He speaks with Pimm Fox on Bloomberg Television's 'Taking Stock.'