Clustering Methods for Credit Card using Bayesian rules based on K-means classification

被引：0

作者：

Saritha, S. Jessica ^{[1
]}

Govindarajulu, P. ^{[2
]}

Prasad, K. Rajendra ^{[3
]}

Rao, S. C. V. Ramana ^{[4
]}

Lakshmi, C. ^{[4
]}

机构：

[1] RGMCET, Dept CSE, Nandyal, AP, India

[2] SV Univ, Tirupati, AP, India

[3] RGMCET, Dept IT, Nandyal, AP, India

[4] RGMCET, Dept MCA, Nandyal, AP, India

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2010年 / 1卷 / 04期

关键词：

Clusters; Probability; K-Means; Thomas Bayesian rule; Credit Card; attributes; banking;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

K-means clustering algorithm is a method of cluster analysis which aims to partition n observations into clusters in which each observation belongs to the cluster with the nearest mean. It is one of the simplest unconfirmed learning algorithms that solve the well known clustering problem. It is similar to the hope 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 joint to analysis the credit card. The analysis result can be used to improve the accuracy.

引用

页码：92 / 95

页数：4

共 50 条

[1] Application of Bayesian rules based on Improved K-means classification on Credit Card
Xie Meiping
[J]. WISM: 2009 INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND MINING, PROCEEDINGS, 2009, : 13 - 16
[2] Using cluster analysis techniques based on K-means and Kohonen clustering methods in credit scoring
Sopko, Stanislav
Soldatyuk, Nataliya
[J]. MATHEMATICAL METHODS IN ECONOMICS (MME 2014), 2014, : 932 - 937
[3] Bayesian hierarchical K-means clustering
Liu, Yue
Li, Bufang
[J]. INTELLIGENT DATA ANALYSIS, 2020, 24 (05) : 977 - 992
[4] Classification of Association Rules based on K-means Algorithm
Dahbi, Azzeddine
Mouhir, Mohamed
Balouki, Youssef
Gadi, Taoufiq
[J]. 2016 4TH IEEE INTERNATIONAL COLLOQUIUM ON INFORMATION SCIENCE AND TECHNOLOGY (CIST), 2016, : 300 - 305
[5] Classification of Moving Vehicles using K-Means Clustering
Changalasetty, Suresh Babu
Thota, Lalitha Saroja
Badawy, Ahmed Said
Ghribi, Wade
[J]. 2015 IEEE INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND COMMUNICATION TECHNOLOGIES, 2015,
[6] Classification of debit card customers based on AHP and K-means
Jiang, Chunxin
Dai, Weidi
Wang, Wenjun
Jia, Yunqiang
[J]. PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MACHINERY, ELECTRONICS AND CONTROL SIMULATION (MECS 2017), 2017, 138 : 73 - 76
[7] Unsupervised Bayesian feature selection based on k-means clustering
Yan, Liu
Yan, Peng
[J]. IC-BNMT 2007: PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON BROADBAND NETWORK & MULTIMEDIA TECHNOLOGY, 2007, : 352 - 356
[8] Credit Card Fraud Detection Using K-Means Combined with Supervised Learning
Jain, Shreyans
Verma, Nishant
Ahmed, Rashid
Tayal, Aman
Rathore, Hemant
[J]. HYBRID INTELLIGENT SYSTEMS, HIS 2021, 2022, 420 : 262 - 272
[9] Classification of Leukocyte Images Using K-Means Clustering Based on Geometry Features
Rosyadi, Tsalis
Arif, Agus
Nopriadi
Achmad, Balza
Faridah
[J]. 2016 6TH INTERNATIONAL ANNUAL ENGINEERING SEMINAR (INAES), 2016, : 245 - 249
[10] Cloud Classification Using Ground Based Images Using CBIR and K-Means Clustering
Rudrappa, Gujanatti
Vijapur, Nataraj
Jadhav, Sushant
Manage, Prabhakar
[J]. BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS, 2020, 13 (13): : 95 - 99

← 1 2 3 4 5 →