The Research on the Data Mining Technology in the Active Demand Management

Author

Xuemei, Chen ; Li, Gao ; Xi, Wang ; Zhonghua, Wei ; Zhenhua, Zhang ; Zhigao, Liao

Author_Institution

Sch. of Mech. & Vehicular Eng., Beijing Inst. of Technol., Beijing, China

fYear

2011

fDate

17-18 Sept. 2011

Firstpage

481

Lastpage

484

Abstract

The traditional K-Means algorithm is sensitive to outliers, outliers traction and easy off-center, and overlap of classes can not very well show their classification. This paper introduces a variant of the probability distribution theory, K-Means clustering algorithm - Gaussian mixture model to part of the customer data randomly selected of Volkswagen dealer in a Beijing office in 2008, for example, and carry out empirical study based on the improved clustering algorithm model. The results showed that: data mining clustering algorithm in active demand management and market segmentation has important significance.

Keywords

Gaussian distribution; data mining; marketing data processing; pattern clustering; supply and demand; Beijing office; Gaussian mixture model; Volkswagen dealer; active demand management; customer data; data mining clustering algorithm; k-mean clustering algorithm; market segmentation; probability distribution theory; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Data mining; Data models; Economics; Educational institutions; K-Means algorithm; active demand management; data mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Internet Computing & Information Services (ICICIS), 2011 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4577-1561-7

Type

conf

DOI

10.1109/ICICIS.2011.125

Filename

6063303