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

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.