In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on comp...In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on competitive leaming. An important property of these algorithms is that they preserve the topological structure of data. This means that data that is close in input distribution is mapped to nearby locations in the network. The FCM algorithm is an algorithm based on soft clustering which means that the different clusters are not necessarily distinct, but may overlap. This clustering method may be very useful in many biological problems, for instance in genetics, where a gene may belong to different clusters. The different algorithms are compared in terms of their visualization of the clustering of proteomic data.展开更多
This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM al...This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.展开更多
Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensio...Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensionality cancer medical database clustering techniques have failed to cluster the available subclasses with less error. Therefore this paper presents suitable fuzzy-based clustering techniques to find available subclasses in high-dimensional prostate and breast cancer databases. In addition this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. In order to evaluate the performance of proposed clustering techniques experimental study has been performed on benchmark databases. Finally the proposed methods have been successfully implemented to find the subclasses of cancers in prostate and breast cancer databases. The clustering results of proposed methods have been validated by evaluating clustering accuracy.展开更多
文摘In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on competitive leaming. An important property of these algorithms is that they preserve the topological structure of data. This means that data that is close in input distribution is mapped to nearby locations in the network. The FCM algorithm is an algorithm based on soft clustering which means that the different clusters are not necessarily distinct, but may overlap. This clustering method may be very useful in many biological problems, for instance in genetics, where a gene may belong to different clusters. The different algorithms are compared in terms of their visualization of the clustering of proteomic data.
文摘This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.
文摘Finding available subclasses in high-dimensional medical databases using clustering techniques is considered as very important one in medical field. Due to similar intensi- ties between the datapoints in high-dimensionality cancer medical database clustering techniques have failed to cluster the available subclasses with less error. Therefore this paper presents suitable fuzzy-based clustering techniques to find available subclasses in high-dimensional prostate and breast cancer databases. In addition this paper presents prototype initialization algorithm to avoid random initialization of initial prototypes. In order to evaluate the performance of proposed clustering techniques experimental study has been performed on benchmark databases. Finally the proposed methods have been successfully implemented to find the subclasses of cancers in prostate and breast cancer databases. The clustering results of proposed methods have been validated by evaluating clustering accuracy.
