Application of Bayesian Rules Based on Improved K-Means Cassification on Credit Card

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 s...

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Bibliographic Details
Published in2009 International Conference on Web Information Systems and Mining pp. 13 - 16
Main Author Xie Meiping
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Algorithm design and analysis
Bayesian methods
Bayesian Rule
Classification algorithms
Clustering algorithms
Credit card
Credit cards
Finance
Information management
K-Means clustering
Management information systems
Partitioning algorithms
Visual databases
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Summary: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.
ISBN:0769538177
9780769538174
DOI:10.1109/WISM.2009.11