关键词: 数据挖掘, 欺诈行为分类, 生长分级自组织特征映射, 灰关联度

Abstract: The network of growing hierarchical self-organizing map (GHSOM) is a kind of variety to the self-organizing map (SOM), it not only can be explained clearly like SOM, but also it’s architecture grows both in a hierarchical and in a horizontal way, and have no use for fixed architecture that has to be defined a-priori, so the problem leading by too much units to deal with is solved, while which ignore the analysis to the importance of each branch in the sample vectors. Thus an improved algorithm is proposed by importing a new gray relation degree between input pattern and the weight vector of the nodes which is trained in each SOM of GHSOM. Making use of this improved algorithm to realize telecom customer’s behavior classification, and get key indexes to predict trick users from classification result, can greatly reduce the dimensions of input space. Demonstrated from the result, classification accuracy can still reach 94.59 percent after taking use of the improved algorithm to reduce the dimensions.

Key words: data mining, trick behavior prediction, GHSOM, gray relation degree