| dc.description.abstract | In the traditional numerical data mining, the stock data is usually compared with fixed value (ex: the stochastic indicator D > 85 or K < 20). The relative comparison between stock information was rarely discussed (ex: the relation between K and D). Thus we propose an extended comparative framework on the numerical data. This framework includes the basic comparison “absolute comparison”. Besides, the “relative comparison” between values is added. The “greater than” and “smaller than” relationship will be obtained then. To advance further, this thesis makes use of understandable C5.0 decision tree classification method. In addition to “absolute comparison” and “relative comparison”, the “variable comparison” of values boundary would be found.
We propose a different framework on data mining method which improves the decision tree to deal with each comparison and do some researches on data comparisons. In this thesis, there are three data types of comparison, and these are: absolute comparison, relative comparison, and variable comparison.
We propose “relative comparison” and “variable comparison” for basic “absolute comparison”. As the result of t test via experiments, the accuracy and precision rate of “absolute + relative comparison” is higher than “absolute comparison”, and the performance of “variable comparison” is better than “absolute + relative comparison” significantly. Hence, this framework not only represents the basic “absolute comparison” of traditional data mining but also discovers diversified “relative comparison” and “variable comparison”. In this framework, potential valuable concept can be found. | en_US |