最后简单介绍了利用计算动词决策树对股票价格数据库进行数据挖掘的过程。
A data mining process for evaluating the database of stocks by using computational verb decision tree was introduced in brief.
对于从包含时间序列的数据库中归纳和提取知识而言，计算动词决策树是一个强大的工具。
Verb decision trees are powerful tools to generalize and extract knowledge from database containing time series.
本文通过对股票交易的决策来说明计算动词决策树对于动态过程历史记录的数据挖掘是非常有效。
In this paper, an example of decision training for exchanging stocks were used to show the usefulness of computational verb decision tree to data mining historical records of dynamical processes.
虽然其在规则设置与参数优化上还是存在着一些不足，但仍可以看出计算动词决策树是一个非常强大和有效的工具。
Though there are some shortages in rules setting and parameter optimization, the computational verb decision trees are powerful and useful tools.
虽然其在规则设置与参数优化上还是存在着一些不足，但仍可以看出计算动词决策树是一个非常强大和有效的工具。
Though there are some shortages in rules setting and parameter optimization, the computational verb decision trees are powerful and useful tools.
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