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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71174

    Title: 以交易時間間隔為基礎之關聯規則分析;Association Rules Mining with Transaction Time-interval
    Authors: 趙潁湞;Jhao,Ying-Jhen
    Contributors: 工業管理研究所
    Keywords: 關聯規則;含時間屬性之關聯規則;時間性交易資料庫;Association Rule Mining;Temporal ARM;Temporal transaction database
    Date: 2016-07-13
    Issue Date: 2016-10-13 12:10:43 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 關聯規則在許多研究中被廣泛的應用與討論,其目的在於從大量數據中挖掘出有價值的數據項之間的相關關係。關聯規則可應用在醫學診斷、生物科技、市場分析、商業決策等。最常被應用於企業的交易資料庫中,用以分析商品之間的關聯性。傳統的關聯規則僅能表達項目之間的相關性,但無法表達購買的時間間隔與購買行為之間的相關性。現今網路購物已成為現代人普遍的購物方式之一,購物的下單時間及購買品項都會被記錄於交易資料庫,若利用傳統的關聯規則分析,無法得知顧客間隔多久消費一次以及下次購買品項為何,便無法適時給予廣告或是促銷活動,刺激消費者購買力以增加利潤。
    ;Association rules mining are widely used in many studies and applications and the aim is to find out the valuable relationships among two itemsets in large database. Association rules can apply in medical diagnostics, biotechnology, market analysis, business decision-making. It commonly be used in business transactions database to analysis the correlations between the items. Traditional association rules can only show the relationships between items but cannot present the correlation among the transaction time-interval and purchase behavior. Nowadays, online shopping has become one of modern popular way to shop that the shopping order time and items purchased will be recorded in the transaction database. Using traditional association rules mining that we cannot know how long the customer will come back to buy and what items they will buy, so we cannot give advertising or promotion in the right time to stimulate consumer purchasing power to increase profits.
    In this study, we consider the transaction time-interval in ARM to discuss the correlation between transaction time-interval and customer behavior. Here, the new rules can know the order of items were purchased and the transaction time-interval length which also take into account the interval is too long to lead the rules become not interested. Therefore, in the process of generating the rules will give a restriction to limit the time-interval length. ARM with time-interval can bring more information to understand the purchasing behavior of customer. For example, how often to go shopping? What will be purchased next time? According to the rules with that we can send different advertisement to different customer at right time to stimulate consumer purchasing power and increase the customer loyalty. We will find out the significance rules with time-interval.
    Appears in Collections:[工業管理研究所 ] 博碩士論文

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