Use of Data Mining for Prediction of Customer Loyalty

Autor:	Andri Wijaya, Abba Suganda Girsang
Jazyk:	angličtina
Rok vydání:	2015
Předmět:	Customer loyalty Attribute analysis C4.5 Naiv¨e Bayes Nearest Neighbor Algorithmghbor algorithms Telecommunication TK5101-6720 Information technology T58.5-58.64
Zdroj:	CommIT Journal, Vol 10, Iss 1, Pp 41-47 (2015)
Druh dokumentu:	article
ISSN:	1979-2484 2460-7010
DOI:	10.21512/commit.v10i1.1660
Popis:	This article discusses the analysis of customer loyalty using three data mining methods: C4.5,Naive Bayes, and Nearest Neighbor Algorithms and real-world empirical data. The data contain ten attributes related to the customer loyalty and are obtained from a national multimedia company in Indonesia. The dataset contains 2269 records. The study also evaluates the effects of the size of the training data to the accuracy of the classification. The results suggest that C4.5 algorithm produces highest classification accuracy at the order of 81% followed by the methods of Naive Bayes 76% and Nearest Neighbor 55%. In addition, the numerical evaluation also suggests that the proportion of 80% is optimal for the training set.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/25e7a406c7f447ca9b560af1401b2961 Zobrazit plný text záznamu View record in DOAJ