Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP...Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.展开更多
A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature ve...A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.展开更多
基金Projects(60903082,60975042)supported by the National Natural Science Foundation of ChinaProject(20070217043)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.
基金Project supported by the Second Stage of Brain Korea 21 ProjectProject(2010-0020163) supported by Priority Research Centers Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education,Science and Technology
文摘A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.
