Titlebook: Intelligent Information and Database Systems; 4th Asian Conference Jeng-Shyang Pan,Shyi-Ming Chen,Ngoc Thanh Nguyen Conference proceedings

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Approach to Image Segmentation Based on Interval Type-2 Fuzzy Subtractive Clusteringased on extension of subtractive clustering algorithm (SC) with fuzziness parameter .. And to manage uncertainty of the parameter ., we have expanded the SC algorithm to interval type-2 fuzzy subtractive clustering (IT2-SC) using two fuzziness parameters .. and .. which creates a footprint of uncert

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Improving Nearest Neighbor Classification by Elimination of Noisy Irrelevant Featuresrea Under the receiver operating characteristics Curve (AUC). The aim of this feature selection is to improve the performance of .-NN algorithm. Experimental results show that the proposed method can substantially improve the classification performance of .-NN algorithm in comparison with the other

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An Efficient Clustering Algorithm Based on Histogram Thresholdtegy called Clustering Algorithm Based on Histogram Threshold (HTCA) is proposed to improve the execution time. The HTCA method combines a hierarchical clustering method and Otsu’s method. Compared with traditional clustering algorithm, our proposed method would save at leastten several times of exe

Diuretic

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Generation of Tag-Based User Profiles for Clustering Users in a Social Music Siteose a method to generate tag-based profiles for clustering users in a social music site. To evaluate our approach, a data set of 1000 users was collected from last.fm, and our approach was compared with conventional track-based profiles. The K-Means clustering algorithm is executed on both user prof

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Cluster Control Management as Cluster Middlewaree benefits of deploying commodity, off-the-shelf hardware (high-power PCs at low prices), using inexpensive high-speed networking such as fast Ethernet, as well as the resulting benefits of using Linux. One of the major difficulty encounters by cluster administrator in the exploitation of Cluster Ne

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A Novel Nonparametric Approach for Saliency Detection Using Multiple Features around each pixel, including intensity, color and orientation. Principal Component Analysis(PCA) is employed to reduce the dimension of the generated feature vector and kernel density estimation is used to measure saliency. We compare our method with five classical methods on a publicly available d

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