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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65191

    Title: 尋找關鍵商品組合之研究;Study on finding key itemsets
    Authors: 陳佩琦;Chen,Pei-chi
    Contributors: 企業管理學系
    Keywords: 資料採礦;data mining;apriori
    Date: 2014-07-29
    Issue Date: 2014-10-15 14:42:58 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著經濟發展,消費者的需求越來越多元,不同消費族群對於商品或服務的需求及對其願付價格不盡相同。在這樣競爭激烈的環境下,企業已不容易將目標市場的所有顧客都攬進自己的市場內。此時,找出對企業有價值的重要顧客成為企業成功獲利的首要步驟。找到重要顧客後,若是能夠發現對重要顧客而言具有價值的商品,即可投其所好,透過滿足關鍵顧客的需求而達到長期盈利的目的。
    ;As economy developing, the demand of consumers getting more and more diverse. Each consumer cluster has its own demand of the product or service and the price they are willing to take. Under such a competitive environment, it is not easy that enterprises occupy the whole target market any more. Figure out who are those important customers, however, be-come the first step to follow for making profit. If it is possible to find out what means the valuable products for those important customers, catering to their pleasure, enterprises can achieve the purpose of making long-tern profit through meeting those key customers’ need.
    The study applies one of the annual transaction record of a medium-sized retailer in Taiwan. Given the transaction data tagged by customer numbers three indicators scores-R,F,M by us-ing RFM analysis method. Then the customer value will be divided low into five degrees by K-means cluster method. The n the study starts mining the key product though the setting of two conditions- minimum support and coefficient of skewness by the modified Apriori al-gorithm.
    To verify the candidate itemsets by minimum support and coefficient of skewness and clas-sify the ones which meet both of the two conditions as the key product. The other ones that only meet the minimum support but not the coefficient of skewness will be classified as in-complete itemsets, combined to be the candidate itemsets for the next stage mining and so on until that finding out any other frequent itemsets is unable to be done. The others that don′ even meet the minimum support will be deleted right away. This study wants to find out the key product(s) that the VIP customers buy frequently through this kind of mining process. And then see the key product(s) as a basis of determining if the new customer is a potential VIP or not.
    The study identified 31 single key product as well as 60 pairs of key products eventually and got up to 69% of the validation ratio
    Appears in Collections:[企業管理研究所] 博碩士論文

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