Improved C4.5 Algorithm for the Analysis of Sales

Author

Cao, Rong ; Xu, Lizhen

Author_Institution

Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China

fYear

2009

fDate

18-20 Sept. 2009

Firstpage

173

Lastpage

176

Abstract

A decision tree is an important means of data mining and inductive learning, which is usually used to form classifiers and prediction models. C4.5 is one of the most classic classification algorithms on data mining, but when it is used in mass calculations, the efficiency is very low. In this paper, the rule of C4.5 is improved by the use of L´Hospital Rule, which simplifies the calculation process and improves the efficiency of decision-making algorithm. When calculating the rate of information gain, the similar principle is used, which improves the algorithm a lot. And the application at the end of the paper shows that the improved algorithm is efficient, which is more suitable for the application of large amounts of data, and its efficiency has been greatly improved in line with the practical application.

Keywords

data mining; decision trees; learning by example; sales management; C4.5 algorithm; L´Hospital rule; data mining; decision tree; inductive learning; sales analysis; Algorithm design and analysis; Application software; Classification algorithms; Classification tree analysis; Computer science; Data mining; Decision making; Decision trees; Information systems; Marketing and sales; algorithm C4.5; decision tree; large data sets; the rate of information gain;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Information Systems and Applications Conference, 2009. WISA 2009. Sixth

Conference_Location

Xuzhou, Jiangsu

Print_ISBN

978-0-7695-3874-7

Type

conf

DOI

10.1109/WISA.2009.36

Filename

5368063