政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/30886

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/30886

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政大機構典藏 > 商學院 > 統計學系 > 學位論文 > Item 140.119/30886

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/30886

题名:	應用資料採礦技術於資料庫加值中的誤差指標及模型準則 ERROR INDEX AND MODEL CRITERIA FOR VALUE- ADDED DATABASE IN DATA MINING
作者:	包寶茹
贡献者:	鄭宇庭謝邦昌包寶茹
关键词:	資料採礦資料加值誤差指標模型準則相似性 Data mining Database value-added Database Error index Model criteria
日期:	2003
上传时间:	2009-09-14
摘要:	運用資料來幫助企業做出正確且適當的政策是一個存在已久的觀念，在傳統統計上我們通常會將拿到的資料庫直接去作分析，然而對資料採礦(Data Mining)來說，常面臨資料不夠的瓶頸，亦導致資料庫的價值往往不夠。若，我們能利用調查的樣本，推估出目標資料庫中所欠缺的欄位在調查樣本中與其它欄位的關係，便可回推至目標資料庫將原本所欠缺的欄位補齊，將資料庫加大，亦即資料加值(value-added)，那麼，未來要用到這些欄位來分析資料時只要抽樣進行分析即可，如此，也可有效降低企業的成本支出或浪費。本研究之目的在於整合過去各學者所提出之統計理論與方法，找出誤差指標及模型準則來說明擴充的欄位是有可信度的。由於在目標資料庫擴充欄位時，會產生誤差值，而誤差值的大小往往會影響我們用來判斷此擴充欄位的可行性及可信度，因此本研究並不考慮使用何種抽樣方法，而是假設在簡單隨機抽樣下來進行探討，判別在資料加值前後所造成預測值與實際值之間的差異情形，進一步來做比較。針對欲擴充目標的欄位型態分為連續型和類別型來尋找適當的指標及準備作為我們選擇判斷的指標。類別型欄位利用相似性觀念建立判斷指標，連續型欄位則利用距離觀念、相關性的架構下來討論，如此，可建立合理的誤差指標及模型準則針對欲擴充目標欄位的型態來判斷其擴充的欄位是否具有可信度，並評估其可用價值的高低。本研究實證結果發現資料庫加值為一可行的方法，從推估資料帶入模式後所得預測值與原始觀測值間計算其相似度皆在九成以上，說明擴充的欄位是有可信度的。關鍵詞：資料採礦、資料加值、誤差指標、模型準則、相似性 In recent years, the application of data mining has received good credits and acceptances from a variety of industries such as the finance industry, the insurance industry, and the electronics industry and so on for its success in extracting valuable information translated to opportunities from the database. Database value-added is a new idea not yet fully mature. Its applications on the different databases will have different effect, therefore, the goal of this research is to find the valid and accountable model criteria as a mean to determine if the added columns make any improvement to the database, hence the overall results in terms of predictions. After selecting the model based upon its appropriateness to the data type, we applied the error index and model criteria to evaluate for the performance of the model, if the model has accurately predicted the added-value column. The criterion used in this research is RMSE for the continuous data type and F-value for the discrete data type. Our findings in this research support our attempts that the error index and model criteria used in this research do give us an accountability measure in determining the reliability of adding the columns to the database. Keywords: Data mining, Database value-added, Database, Error index, Model criteria
參考文獻:	[1] Anderson, Sweeney, Williams (2002), “Statistics for business and economics”, Eighth edition, South-Western, pp.616-617 [2] Alan Agresti (1996), “An Introduction To Categorical Data Analysis”, A Wiley- Interscience Publication, pp.103-135 [3] Berry, Michael (1997), “Data mining techniques : for marketing, sales, and customer support” New York : John Wiley & Sons [4] Christine Michel (2001), “Ordered similarity measures taking into account the rank of documents”, Information Processing & Management, Vol. 37, Iss. 4; p.603 [5] Carol , Tenopir (1990),“Online databases” Library journal, April [6] Han, Jiawei and Kamber, Micheline (2001) “Data mining : concepts and techniques” San Francisco, Calif. : Morgan Kaufmann Publishers [7] Jin Zhang and Robert R.Korfhage (1999), “A distance and angle similarity measure method ”, Journal of the American society for information science, pp.772-778 [8] Jo-Chun Cheng (2003), “A Study of Similarity Measures of DNA Sequences under Evolution.” [9] Kantardzic, Mehmed (2003), “Data mining: concepts, models, methods and algorithms” Hoboken, N.J.: Wiley-Interscience : IEEE Press. [10] Kim, Myoung-Cheol, Choi, Key-Sun (1999), “A comparison of collocation- based similarity measures in query expansion”, Information Processing & Management, Vol. 35, Iss. 1; p.19 (12 pages) [11] Min-Te Chao (2003), “Some Ideas for data base fusion” draft. [12] Michael E Young, Edward A Wasserman (2002), “Limited attention and cue order consistency affect predictive learning: A test of similarity measures”, Journal of Experimental Psychology, Vol. 28, Iss. 3; p. 484 [13] Mathews, Brian P; Diamantopoulos, Adamantios (1994), “Towards a taxonomy of forecast error measures: A factor-comparative investigation of forecast error dimensions” Journal of Forecasting, Vol.13, pp.409-416 [14] Young Mee Chung, Jae Yun Lee (2001), “A corpus-based approach to comparative evaluation of statistical term association measures” Journal of the American society for information science and technology, pp.283-296
描述:	碩士國立政治大學統計研究所 91354017 92
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0091354017
数据类型:	thesis
显示于类别:	[統計學系] 學位論文

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