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This paper addresses the problem of diagnosing the fault symptoms of power transformers with measurement originated uncertainties, which arise from the imprecision of samples (i.e. due to noises and outliers) and the effect of class imbalance (i.e. samples are unequally distributed between different fault types) in a training dataset used to identify different fault types. Two fuzzy support vector...
The voluminous data analysis is an obstacle for video indexing and retrieval, a novel method based on video frame difference is proposed to make the fast indexing: firstly frame clustering with FSVM is used to extract the important scene in video; secondly the scenes are labelled with characteristic features; finally, the associated rule data-mining is used to fabricate the last video analysis. The...
Experiments are carried out on datasets with different dimensions selected from UCI datasets by using two classical clustering algorithms. The results of the experiments indicate that when the dimensionality of the real dataset is less than or equal to 30, the clustering algorithms based on distance are effective. For high-dimensional datasets--dimensionality is greater than 30, the clustering algorithms...
In this paper, the necessity of ranking of the enterprise's informatization capacity maturity is researched on the basis of EICMM (Enterprise Informatization Capacity Maturity Model). Based on the EICMM, we adopt the proper method (Cluster Analytical Method) for the empirical research, commence from the evaluated index system, design the questionnaires and collect the related data of informatization,...
A clustering based composite kernels support vector machine ensemble forecasting model is proposed for the chaotic time series prediction. First, fuzzy possibility c-mean clustering algorithm (FPCM) is adopted to partition the input dataset into several subsets, which can overcome the drawback caused by outlier and noise in conventional fuzzy c-mean method. Then, SVMs with composite kernels that best...
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