The study of value-added database evaluation in data mining
Date Issued
2008
Date
2008
Author(s)
Yeh, Ruey-Ling
Abstract
Data plays a vital role as a source of information to organizations, especially in times of information and technology. One encounters a not-so-perfect database from which data is missing or insufficient, and the results obtained from such a database may provide biased or misleading solutions. Therefore, imputing missing data and functional mapping to a database has been regarded as one of the major steps in data mining.A goal database and an auxiliary database utilizing functional mapping make the database combine as a great database, the purpose of this research is to evaluate the structure of the data when the database has been value-added. The present research used different methods of data mining to construct imputative and value-added models in accordance with different types of data. When the missing data is continuous, regression models and Neural Networks are used to build predictive models. For the categorical missing data, the logistic regression model, neural network, C5.0 and CART are employed to construct predictive models. n this research use RMSE , accuracy rate and Kappa statistic to examine the results of imputation and value-added database. The results showed that the regression model was found to provide the best estimate of continuous data; but for categorical data, the C5.0 model proved the best method.After the assessment of the data, using the imputation and functional mapping makes the database add value and increase the amount of information of the data. The value-added database really has its effect because the increase of the amount of information is good for the database that will carry on data mining.
Subjects
Data mining
Missing data
Imputation
Functional Mapping
Value-added database
Type
thesis
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