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In document categorization method by using similarity measures based on word vectors, it is important to determine key words to characterize each document. However, conventional methods select the key words based on their frequency or/and particular importance index such as tf-idf. In this paper, we propose a method to characterize each document by using temporal clusters of technical term usages...
A new clustering classification approach based on fuzzy closeness relationship (FCR) is studied in this paper. As we know, fuzzy clustering classification is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering classification is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the...
Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space in which class distribution is impossible to visualize. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining...
Neural network is a widely used and an effective artificial intelligence technique used for predictions and classifications which has been developed based on human biological neural system. Determining the structure of a neural network is a very complex task and there is no defined approach to determine the structure, especially the number of hidden nodes. Traditionally the number of hidden nodes...
An important approach to unsupervised pixel classification in remote sensing satellite imagery is to use clustering in the spectral domain. In this article, a recently proposed multiobjective fuzzy clustering scheme has been combined with artificial neural networks (ANN) based probabilistic classifier to yield better performance. The multiobjective technique is first used to produce a set of non-dominated...
This paper presents a novel rough-based feature selection method for gene expression data analysis. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers that approximate to the correct ones. In this paper, we attempt to introduce a prediction scheme that combines the rough-based feature selection method with radial basis function...
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