Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically...Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.展开更多
With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzz...With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzzy clustering theory and deep neural network. The focus of data mining in seeking the visualization methods in the process of data mining, knowledge discovery process can be users to understand, to facilitate human-computer interaction in knowledge discovery process. Inspired by the brain structure layers, neural network researchers have been trying to multilayer neural network research. The experiment result shows that out algorithm is effective and robust.展开更多
The aim of this paper is to adopt two-stage classification methods, and to apply fuzzy clustering analysis for mining data in the credit market in order to reflect the characteristic type knowledge of common nature of...The aim of this paper is to adopt two-stage classification methods, and to apply fuzzy clustering analysis for mining data in the credit market in order to reflect the characteristic type knowledge of common nature of the similar things and different type characteristic knowledge of dissimilar things. First of all, the paper carries on attribute normalization of multi-factors which influence banks credit, computes fuzzy analogical relation coefficient, sets the threshold level to α by considering the competition and social credit risks state in the credit market, and selects borrowers through transfer closure algorithm . Second, it makes initial Classification on samples according to the coefficient characteristic of fuzzy relation; third, it improves fuzzy clustering method which the fussy clustering itself has fuzzy nature and the algorithm. Finally the paper provides a case study about knowledge of credit mining in the financial market.展开更多
基金Funded by the National 973 Program of China (No.2003CB415205)the National Natural Science Foundation of China (No.40523005, No.60573183, No.60373019)the Open Research Fund Program of LIESMARS (No.WKL(04)0303).
文摘Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.
文摘With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzzy clustering theory and deep neural network. The focus of data mining in seeking the visualization methods in the process of data mining, knowledge discovery process can be users to understand, to facilitate human-computer interaction in knowledge discovery process. Inspired by the brain structure layers, neural network researchers have been trying to multilayer neural network research. The experiment result shows that out algorithm is effective and robust.
文摘The aim of this paper is to adopt two-stage classification methods, and to apply fuzzy clustering analysis for mining data in the credit market in order to reflect the characteristic type knowledge of common nature of the similar things and different type characteristic knowledge of dissimilar things. First of all, the paper carries on attribute normalization of multi-factors which influence banks credit, computes fuzzy analogical relation coefficient, sets the threshold level to α by considering the competition and social credit risks state in the credit market, and selects borrowers through transfer closure algorithm . Second, it makes initial Classification on samples according to the coefficient characteristic of fuzzy relation; third, it improves fuzzy clustering method which the fussy clustering itself has fuzzy nature and the algorithm. Finally the paper provides a case study about knowledge of credit mining in the financial market.
