基于改进的K-means聚类算法的汽车市场竞争情报分析

Information analysis of auto market competition based on improved K-means cluster algorithm

应用AHP(analytic hierarchy process)和EWM(entropy weight method),对中国A级轿车市场数据进行了分析量化处理,设计了竞争威胁数据指标,基于改进的K-means聚类算法对该市场进行了社会网络分析;通过品牌间竞争矩阵构建了中间中心度及凝聚子群,分析了产品性能指标偏重程度和企业所在该市场的竞争地位。数值实验表明:改进的K-means聚类算法对于文中样本对象,得到了更为精确的聚类效果,对中国A级轿车市场的社会网络分析准确有效。

Analytic hierarchy process(AHP)and entropy weight method(EWM)were used to analyze and quantify the data of China's A-level auto market and a data index of competition threat was designed.Social network analysis of the market was carried out based on the improved K-means cluster algorithm.Between-centrality and cohesive-subgroup were constructed through competition matrix among brands.An analysis was made of the degree of product performance index and competition status of the enterprise's relevant market.The numerical experiment shows the improved K-means cluster algorithm is comparatively effective to social network analysis of China's A-level auto market.