政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/30885

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/30885

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政大典藏 > College of Commerce > Department of Statistics > Theses > Item 140.119/30885

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/30885

Title:	應用資料採礦技術於資料庫加值中的抽樣方法 THE SAMPLING METHODS FOR VALUE-ADDED DATABASE IN DATA-MINING
Authors:	陳惠雯
Contributors:	鄭宇庭謝邦昌陳惠雯
Keywords:	資料庫資料採礦抽樣方法資料加值 Database Data Mining Sampling Value-added database
Date:	2003
Issue Date:	2009-09-14
Abstract:	In the wake of growing database that has already become the trend of today’s business environment within the foreseeable future, reviewing quality information from mountains of data residing on corporations or organizations’ network such as sales figures, manufacturing statistics, financial data and experimental data is clearly costly, time consuming and definitely ineffective approach. Therefore we would need a sound and effective method in obtaining only portions of the data that are representative to the population and which allow us to build the reliable model based upon the sampled data. However, sometimes we have a situation where the database is of limited in size, under such circumstance, we initiate the idea which is relatively new to adding the attributes or values into the database to enhance the quality of the data Follow through such a procedure; it is obvious that implementing a good sampling method is an important groundwork leading us to reach final destination that is obtaining a reliable predictive model. And this is our research goal that is to get an effective and representative value-added sample of by means of sampling method for building an accuracy predictive model. The concept is pretty straightforward that is if we want to get good predictive samples then we need the correct sampling methods. The sampling methods under study are simple random sample, system sample, stratified sample and uniform design. The models used are the C5.0, logistic regression, and neural network for categorical predictive variable and stepwise regression for continuous predictive variable. The results are discussed in the conclusion section. Keywords: Database、Data Mining、Sampling、Value-added database
Reference:	Chinese [1] 趙民德、謝邦昌，探索真相-抽樣理論和實務，曉園出版社，1999. [2] 黃文隆，抽樣方法，滄海書局，1999. [3] 趙民德，砂中選礦（Data Mining）的一些我見我思，中國統計學報，2002，12. [4] 王濟川、郭志剛，Logistic 迴歸模型-方法及應用，五南圖書出版股份有限公司，2003，3. [5] 崔巍編著，陳舜德審校，資料庫系統與應用，博碩文化股份有限公司， 2001，4. [6] 張慶賀，資料倉儲中實體化視域自我維護之研究，朝陽科技大學，2003. English [1] Alan Mayne，Michael B Wood，Introducing Relational Database，1983. [2] Bernd Gartner and Emo Welzl，A Simple Sampling Lemma: Analysis and Applications in Geometric Optimization，2002，4. [3] Colleen McCue、Emilys. Stone、Teresap. Gooch，Data Mining and Value-Added Analysis，2003. [4] CHAP T. LE，APPLIED CATEGORICAL DATA ANALYSIS，Wiley-Interscience Publication，1998. [5] C. J. Date，Relational Database Writings 1991-1994，1995. [6] David Hand、Heikki Mannila、and Padhraic Smyth，PRINCIPLES OF Data Mining，2001. [7] Laboratory 2: Ecological population: a crash course in sampling and statistics. [8] Margaret H.Dunham，DATA MINING Introductory and Advanced Topics，2003. [9] Saerndal Carl-Erik、Bengt Swensson、Jan Wretman，Model Assisted Survey Sampling，New York: Springer-Verlag，1992. [10] USDA Technical Services Division: GRAIN INSPECTION PACKERS AND STOCKYARDS ADMINISIRATION，2001，1. [11] William Mendenhall、Terry Sincich，A SECOND COURSE IN STATISTICS REGRESSION ANALYSIS，PRENTICE FALL，fifth edition，1996.
Description:	碩士國立政治大學統計研究所 91354016 92
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0091354016
Data Type:	thesis
Appears in Collections:	[Department of Statistics] Theses

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