英文摘要 |
In this paper, we use consumers' transaction data as the source data of mining. Each transaction data contains a consumer ever bought product items with quantity. We let some product items as the target of mining, and regard other products as attribute items for classification. We discover the most adaptive marketing mix of the product items from two aspects. First, we only consider product items whether they are contained in transaction data or not. In the mining process, we compute the percentage of transaction data contains the product items, and assign the association degree between the transaction data and the product items to be "high" if the percentage satisfies the minimum association threshold. Otherwise, it is "low". We classify the transaction data to construct a decision tree, and find out the attribute items that have the association degree to be high with the product items according to the decision tree. It is the basis to discover the most adaptive marketing mix of the product items. Moreover, we extra consider product items with quantity in the transaction data. In the mining process, we compute the percentage of transaction data contains the product items, and assign the association degree between the transaction data and the product items to be "high" if the percentage satisfies the minimum quantitative association threshold. Otherwise, it is "low". We propose a method to classify the transaction data with quantitative items for constructing a decision tree, and find out the attribute items that have the association degree to be high with the product items according to the decision tree. It is the basis to discover the most adaptive marketing mix with quantitative items of the product items. The results of the mining can provide very useful information to plan the strategy of marketing mix of products for the business. |