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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陳惠雯 Unknown Date (has links)
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
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應用資料採礦技術於資料庫加值中的插補方法比較 / Imputation of value-added database in data mining黃雅芳 Unknown Date (has links)
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。 / 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.
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