This paper presents an advanced fuzzyc-means(fcm) clusteringalgorithm to overcome the weakness of the traditional fcmalgorithm, including the instability of random selecting of initial center and the limitation of ...
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This paper presents an advanced fuzzyc-means(fcm) clusteringalgorithm to overcome the weakness of the traditional fcmalgorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced fcmalgorithmcombines the distance with density and improves the objective function so that the performance of the algorithmcan be improved. The experimental results show that the proposed fcmalgorithm requires fewer iterations yet provides higher accuracy than the traditional fcmalgorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%.
In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage o...
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In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and fuzzyc-means (fcm) clusteringalgorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-c/X-SAR (Spaceborne Imaging Radar-c/X-band Synthetic Aperture Radar) data.
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