Title :
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
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;
Conference_Titel :
Internet Computing & Information Services (ICICIS), 2011 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4577-1561-7
DOI :
10.1109/ICICIS.2011.125
