Title :
Application of Bayesian Rules Based on Improved K-Means Cassification on Credit Card
Author_Institution :
Sch. of Inf. Manage. & Eng., Shanghai Univ. of Finance & Econ., Shanghai, China
Abstract :
K-means clustering algorithm is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data. Bayesian rule is a theorem in probability theory named for Thomas Bayesian. It is used for updating probabilities by finding conditional probabilities given new data. In this paper, K-mean clustering algorithm and Bayesian classification are combined to analysis the credit card. The analysis result can be used to improve the accuracy.
Keywords :
Bayes methods; Gaussian processes; credit transactions; expectation-maximisation algorithm; pattern classification; pattern clustering; probability; unsupervised learning; Bayesian rules; Gaussian mixture algorithm; K-means clustering algorithm; cluster analysis method; credit card; expectation-maximization algorithm; improved k-means classification; probability theory; simplest unsupervised learning algorithms; Algorithm design and analysis; Bayesian methods; Classification algorithms; Clustering algorithms; Credit cards; Finance; Information management; Management information systems; Partitioning algorithms; Visual databases; Bayesian Rule; Credit card; K-Means clustering;
Conference_Titel :
Web Information Systems and Mining, 2009. WISM 2009. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3817-4
DOI :
10.1109/WISM.2009.11
