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To solve the class imbalance problem in classification of pre-miRNAs with ab initio method, a novel sample selection method is proposed according to the characteristics of pre-miRNAs. Real/pseudo pre-miRNAs are clustered based on their stem similarity and their distribution in high dimensional sample space respectively. The training samples are selected according to the sample density of each cluster...
In this paper, a novel clustering-based classifier using Support Vector Machines criterion (called CBCSVM) is presented for pattern classification. This algorithm involves three steps. At first, the robust clustering algorithm Kernelized Fuzzy c-means is utilized to yield the clustering centers. Then, a set of Gaussian functions associated with these obtained centers are adopted to map the samples...
In this paper, a Gaussian Mixture Model-based clustering algorithm using dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algortihm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints...
Clustering analysis to identify hot spots of interest is a popular topic in spatial data mining. One of the challenges for existing clustering approaches is that they are not suitable for clustering high-dimensional feature vectors. Many existing density-based clustering algorithms model the point density based on two-dimensional information without considering the impact of the vertical dimension...
The key to the implementation of dynamic forensics is how to mine in real-time and effectively criminal invasion information from voluminous data. Towards the disadvantages of Fuzzy C-means clustering (referred to as FCM) forensics analysis that it is very sensitive to initial data and impacted greatly by noise, a dynamic forensics analysis technology based on genetic-fuzzy clustering algorithm is...
In this paper, a fuzzy clustering algorithm using dependable neighbor pixels is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algorithm utilizes the local statistical information to discriminate dependable neighbor pixels from undependable neighbor pixels, and then allows the labeling of the pixel to be influenced by the dependable neighbor pixels...
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