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研究生:劉冠緯
研究生(外文):Guan-Wei Liou
論文名稱:應用時間加權於可重複序列之研究-以預測線上顧客消費狀態為例
論文名稱(外文):The Application of Time-weighted Concept to Re-counting Sequence
指導教授:羅淑娟羅淑娟引用關係
口試委員:林晶璟葉瑞徽
口試日期:2007-06-12
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:53
中文關鍵詞:二元序列樣式探勘時間加權模式可重複序列
外文關鍵詞:Sequential Patterns MiningTime-weightedRe-counting Sequence
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在以一對一滿足顧客需求為競爭關鍵的時代中,企業管理者若能有效掌握顧客的消費行為,適時提供正確的服務來滿足顧客的需求,便能提升企業競爭優勢,也因此瞭解顧客消費行為的變化就成為企業獲利與否的關鍵因素。而近年來序列樣式探勘為探討顧客行為規則的研究焦點之一,但是鮮少於討論不同的時間點影響不同程度的行為變化之議題。有鑑於此,本研究將建構一預測顧客行為狀態的模式,此模式結合了時間加權概念與可重複序列樣式探勘,其中,時間加權概念主要目的是為了解決顧客行為狀態會隨著時間週遭因素的影響,而使得序列資料的變異性較大,而導致分析結果表現較不為理想。而可重複序列則可以單獨對一顧客序列作探勘,這樣可以針對高價值或高忠誠度顧客進行一對一的分析,以提高顧客滿意度。
本研究以國內某線上音樂公司為例,預測顧客其次時點的消費狀態。在時間加權方面導入了越近期資料給予越大權重和同屬性期間加權,管理者便可依照時間不同的重要程度將顧客序列予以分割,並且給予不同的權重大小,以產生新的且符合管理者所需的預測規則,線上公司可依此預測規則針對顧客做預先個人化服務配置,對線上公司在提升顧客服務行動上,提出一具體可行方案。
In this period when we regard the satisfaction of one to one customer’ needs as a key to competition, we can raise the advantage for business competition if the business managers can effectively control the consuming behavior of customers and offer right services to satisfy customers’ needs at an appropriate time. Therefore, it becomes the essential factor in the profit of enterprises that we understand the change of consuming behavior of customers. In recent years, sequential pattern mining has been one of the research focuses on the rule of customer’s behavior. Nevertheless, it has been hardly discussed that different timing could affect the change of behavior to some extent. Because of that, this research will construct a model of predicting the state of customer’s behavior, which combines the time-weighted concept and re-counting sequence mining. In this model, the main purpose of time-weighted concept is to resolve the problem that the status of customer’s behavior varies in time, which results in obvious variety of sequence data to make analysis performances less desirable. And the re-counting sequence can mine for single customer-sequence, which is able to analyze those high-value or highly loyal customers in a one to one way and raise customer satisfaction. This method divides customer-sequence into several partitions according to time and gives each partition different weight to produce new prediction rules that interest managers.
摘要 i
Abstract ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 5
1.4 研究流程 5
第二章 文獻探討 7
2.1 關聯規則 7
2.1.1 關聯規則的定義 7
2.1.2 關聯規則的應用 9
2.1.3 Apriori 演算法 9
2.2 序列樣式探勘 12
2.2.1 序列樣式探勘的分類 13
2.2.2 探勘高頻序列樣式的主要方法 15
2.2.3 兩類高頻探勘技術的比較 17
2.3 二元序列樣式探勘 18
2.3.1 二元可重複序列 18
2.3.2 二元序列演算法 19
2.3.3 產生預測用的序列樣式 23
2.3.4 預測的機制 25
第三章 研究方法 27
3.1 漸進式加權探勘 27
3.2 應用PWM於二元序列樣式探勘 31
第四章 實驗分析 38
4.1 資料來源與資料整理 38
4.2 實驗環境參數選擇與設定 41
4.3 分析與結果 43
4.4 總結 47
第五章 結論與建議 48
5.1 研究結論 48
5.2 研究貢獻 48
5.3 未來研究方向 49
參考文獻 50
[中文部分]
[1]李昌鴻,時序性資料庫之關聯性勘測,博士論文,國立臺灣大學電機工程學研究所,台北,2002。
[2]張維捷,以資料挖礦法則預測網頁更新規則之研究,碩士論文,國立中央大學企業管理研究所,桃園,1999。
[3]陳仕昇,序列樣式探勘之研究,博士論文,國立國立中央大學資訊管理研究所,桃園,2002。
[4]陳仕昇、陳彥良、許秉瑜 ,”以可重複序列探勘網路瀏覽規則之研究,”資管評論,第九期 1999。
[5]楊文昇,有效率的探勘關聯法則之高頻物項集合演算法,碩士論文,逢甲大學資訊工程學系,台中,2000。
[6]楊俊能,以序列樣式探勘技術預測線上顧客次時點消費狀態之研究,碩士論文,國立台北科技大學生產系統工程與管理研究所,台北,2004。
[7]龔書賢,應用基因演算法及權重項目法於關聯法則挖掘之研究,碩士論文,元智大學工業工程與管理學系,桃園,2003。
[英文部分]
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[22]Pinto, H. , Han, J. , Pei, J. , Wang, K. , Chen, Q. and Dayal, U. , “Multi-dimensional sequential pattern mining,” in proc. of the 10th International Conference on Information and Knowledge Management, Atlanta, Georgia, 2001.
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[24]Tao, F.,” Weighted Association Rule Mining using Weighted Support and Significant framework,” in proc. ACM SIGKDD, Aug. 2003.
[25]Valarie, A. Z., Roland, T. R. and Katherine, N. L., “The Customer Pyramid: Creating and Serving Profitable Customers,” California Management Review ,Vol.43, No.4, 2001, pp.118-142.
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