| dc.contributor.advisor | 鄭宇庭<br>謝邦昌 | zh_TW |
| dc.contributor.advisor | <br> | en_US |
| dc.contributor.author (Authors) | 黃雅芳 | zh_TW |
| dc.creator (作者) | 黃雅芳 | zh_TW |
| dc.date (日期) | 2003 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (Other Identifiers) | G0091354018 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30887 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 91354018 | zh_TW |
| dc.description (描述) | 92 | zh_TW |
| dc.description.abstract (摘要) | 資料在企業資訊來源中扮演了極為重要的角色，特別是在現今知識與技術的世代裡。如果對於一個有意義且具有代表性資料庫中的遺漏值能夠正確的處理，那麼對於企業資訊而言，是一個大有可為的突破。 然而，有時我們或許會遇到一些不是那麼完善的資料庫，當資料庫中的資料有遺漏值時，從這樣資料庫中所獲得的結果，或許會是一些有偏差或容易令人誤解的結果。因此，本研究的目的在於插補遺漏值為資料庫加值，進而根據遺漏值類型建立插補模型。 如果遺漏值為連續型，用迴歸模型和倒傳遞類神經模型來進行插補；如果遺漏值為類別型，採用邏輯斯迴歸、倒傳遞類神經和決策樹進行插補分析。經由模擬的結果顯示，對於連續型的遺漏值，迴歸模型提供了最佳的插補估計；而類別型的遺漏值，C5.0決策樹是最佳的選擇。此外，對於資料庫中的稀少資料，當連續型的遺漏值，倒傳遞類神經模型提供了最佳的插補估計；而類別型的遺漏值，亦是C5.0決策樹是最佳的選擇。 | zh_TW |
| dc.description.abstract (摘要) | Data plays a vital role as source of information to the organization especially in the era of information and technology. A meaningful, qualitative and representative database if properly handled could mean a promising breakthrough to the organizations. However, from time to time, we may encounter a not so perfect database, that is we have the situation where the data in the database is missing. With the incomplete database, the results obtained from such database may provide biased or misleading solutions. Therefore, the purpose of this research is to place its emphasis on imputing missing data of the value-added database then builds the model in accordance to the type of data. If the missing data type is continuous, regression model and BPNN neural network is applied. If the missing data type is categorical, logistic regression, BPNN neural network and decision tree is chosen for the application. Our result has shown that for the continuous missing data, the regression model proved to deliver the best estimate. For the categorical missing data, C5.0 decision tree model is the chosen one. Besides, as regards the rare data missing in the database, our result has shown that for the continuous missing data, the BPNN neural network proved to deliver the best estimate. For the categorical missing data, C5.0 decision tree model is the chosen one. | en_US |
| dc.description.tableofcontents | LIST OF TABLES……………………………………………………………iii LIST OF FIGURES……………………………………………………………v 1 Introduction………………………………………………………………1 1.1 Research Background………………………………………………1 1.2 Research Motive and Purpose……………………………………3 1.3 Research Procedure…………………………………………………4 1.4 Research Outlay……………………………………………………5 2 Literature Review………………………………………………………6 2.1 Database and Data Warehouse……………………………………6 2.2 Relational Database………………………………………………7 2.3 Data Mining…………………………………………………………9 2.4 Introduction to Missing Data…………………………………12 2.5 introduction to Rare data………………………………………13 2.6 Imputation Methods………………………………………………14 2.7 The Predictive Model for the imputation……………………21 2.7.1 Regression Model…………………………………………21 2.7.2 Logistic Regression Model………………………………22 2.7.3 Artificial Neural Network………………………………23 2.7.4 Decision Tree………………………………………………27 3 Research Methodology…………………………………………………30 3.1 Research Concept…………………………………………………30 3.2 Research Frame……………………………………………………31 4 Evaluating Performance………………………………………………34 4.1 Data understanding………………………………………………34 4.2 Data preparation…………………………………………………35 4.3 Modeling……………………………………………………………38 4.4 Evaluation…………………………………………………………54 5 Conclusions and Suggestions…………………………………………76 5.1 Conclusions…………………………………………………………76 5.2 Suggestions…………………………………………………………78 References……………………………………………………………………82 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0091354018 | en_US |
| dc.subject (關鍵詞) | 資料採礦 | zh_TW |
| dc.subject (關鍵詞) | 資料庫加值 | zh_TW |
| dc.subject (關鍵詞) | 稀少資料 | zh_TW |
| dc.subject (關鍵詞) | 遺漏值 | zh_TW |
| dc.subject (關鍵詞) | 插補 | zh_TW |
| dc.subject (關鍵詞) | data mining | en_US |
| dc.subject (關鍵詞) | value-added database | en_US |
| dc.subject (關鍵詞) | rare data | en_US |
| dc.subject (關鍵詞) | missing data | en_US |
| dc.subject (關鍵詞) | imputation | en_US |
| dc.title (題名) | 應用資料採礦技術於資料庫加值中的插補方法比較 | zh_TW |
| dc.title (題名) | Imputation of value-added database in data mining | en_US |
| dc.type (資料類型) | thesis | en |
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| dc.relation.reference (參考文獻) | 9. Steven Roman（2002）Access Database Design & Programming. O"Reilly | zh_TW |
| dc.relation.reference (參考文獻) | 10. Hyunyoon Yun, Danshim Ha, Buhyun Hwang and Keun Ho Ryu (2003) Mining association rules on significant rare data using relative support. The journal of systems and software 67. pp181-191 | zh_TW |
| dc.relation.reference (參考文獻) | 11. John O. Rawlings, Sastry G. Pantula and David A. Dickey（1998） Applied Regression Analysis---A Research Tool, 2nd ed. Springer. | zh_TW |
| dc.relation.reference (參考文獻) | 12. Roderick J.A. Little and Donald B. Rubin（2002） Statistical Analysis with Missing Data, 2nd ed. Wiley interscience. | zh_TW |
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