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With the hypothesis of Gaussian distribution of patterns, K-means and its extensions are good for clustering. As the representative of partitional clustering algorithm, K-means follows rules for running: numbers of clusters to be set, cluster initialization to be specified and certain objective function to be optimized. In general, FCM, ANN, EM share the identical idea with K-means in the beginning...
SOM neural network is one of the most commonly used Clustering algorithm in the text clustering. The initial connection weights of SOM neural network will affect the degree of convergence. If the Initial connection weights are not set appropriate, that will cause in a long wandering around the local minimum, accordingly lower the speed of convergence, or even cause local convergence or not convergence...
TurSOM is a novel self-organizing map algorithm with the capability of connection reorganization, not just neuron reorganization. This behavior facilitates the ability to map distinct patterns in a given input space. Multiple networks exist, and operate independently. This work presents an application driven approach, based on the theoretical and empirical work of previous TurSOM experiments. TurSOM...
This paper proposes a hierarchical architecture, HieNet, that utilizes the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to...
In this paper, we propose a fast C-means based training of Fuzzy Hopfield neural network and apply it to image segmentation. According to the other ways which usually take a long time, we define a fast method for image segmentation. We present a new objective function, and its minimization by Lyapunov energy function which is based on two dimensional fuzzy Hopfield neural network. This objective function...
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