基于主成分分析的特征选择驱动店铺选择:一种数据挖掘方法

R. Mittal, A. Mittal, Jaiteg Singh, Vikas Rattan, Varun Malik
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引用次数: 2

摘要

商店选择是商店形象的函数,而商店形象又由商店属性组成。顾客对不同的商店属性有不同的评价。对于研究人员和管理人员来说,了解购物者如何评估商店的多种属性并不容易。需要将大量的属性减少到更易于管理的数量,这可以使用特征选择或因子分析的数据挖掘技术来完成。一旦应用了这种数据挖掘技术,就可以对新出现的因素进行处理,从而更好地理解购物者的商店选择标准。本研究评估了印度大型超市的197名购物者评估的23个商店属性,这些属性被减少到7个因素。对这七个因素进行了排序。价格/价值相关因素排名最高。
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Principal Component Analysis based Feature Selection Driving Store Choice: A Data Mining Approach
Store choice is a function of store image which in turn comprises of store attributes. Different store attributes are evaluated differently by shoppers. For researchers and managers, it is not easy to understand how shoppers assess the multiple attributes that a store has. The high number of attributes needs to be reduced to a more manageable number and this can be done using the data mining technique of feature selection or factor analysis. Once this data mining technique is applied, the emerging factors can be processed to understand shoppers store choice criteria much better. This study assesses 23 store attributes evaluated by 197 shoppers of hypermarkets in India which were reduced to seven factors. The seven factors were ranked. Price / Value related factor was ranked highest.
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