This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels fro...This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.展开更多
Clustering analysis plays a very important role in the field of data mining,image segmentation and pattern recognition.The method of cluster analysis is introduced to analyze NetEYun music data.In addition,different t...Clustering analysis plays a very important role in the field of data mining,image segmentation and pattern recognition.The method of cluster analysis is introduced to analyze NetEYun music data.In addition,different types of music data are clustered to find the commonness among the same kind of music.A music data-oriented clustering analysis method is proposed:Firstly,the audio beat period is calculated by reading the audio file data,and the emotional features of the audio are extracted;Secondly,the audio beat period is calculated by Fourier transform.Finally,a clustering algorithm is designed to obtain the clustering results of music data.展开更多
基金Supported by the Fundamental Research Funds for the Central Universities in North China Electric Power University(11MG13)the Natural Science Foundation of Hebei Province(F2011502038)
文摘This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.
基金Thisre search was partially supported by the National Natural Science Foundation of China(grant 62076215)the Talent Introduction Project of Yancheng Institute of Technology under Grant No.XKR2011019.
文摘Clustering analysis plays a very important role in the field of data mining,image segmentation and pattern recognition.The method of cluster analysis is introduced to analyze NetEYun music data.In addition,different types of music data are clustered to find the commonness among the same kind of music.A music data-oriented clustering analysis method is proposed:Firstly,the audio beat period is calculated by reading the audio file data,and the emotional features of the audio are extracted;Secondly,the audio beat period is calculated by Fourier transform.Finally,a clustering algorithm is designed to obtain the clustering results of music data.
