数据采掘 meaning in Chinese
data excavating
data mining
Examples
- With the boom of date mining , bayesian network has been paid great attention and again in the limeligh < because of the naturai relation between the mathematical statistics and data mining
由于概率统计与数据采掘的天然联系。数学采据兴起后贝叶斯网络日益受到重视，再次成为引人注目的热点 - Therefore , it is the same with data mining with probability statistic character and knowledge discovery problems , especially with die problems that obtain sample information or need high cost
因此，适用于具有概率统计特征的数据采掘和知识发现问题，尤其是样本难以获取或代价过于昂贵的问题。 - Comparing with non - bnyain methods , it ' s prominent featares lay in that it combines the prior and posterior information , which avoids the disadvantag of subjective bias caused by simply using the prior information only , of blind search caused by the incomplete sample information , of noise affection caused by simply using the sample information only if we choice a suitable priof , we can conduct the bayesian leaming effectively , so it fits the problems of data mining and machine leaming that possess charaters of probability and statistics , especially when the samples are rare
与非贝叶扬方法相比，贝叶斯方法的特出特点是其学习机制可以综合先验信息和后验信息，既可避免只使用先验信息可能带来的主观偏见，和缺乏样本信息时的大量盲目搜索与计算，也可避免只使用样本信息带来的噪音的影响只要合理地确定先验，就可以进行有效的学习。因此，适用于具有概率统计特征的数据采掘和机器学习（或发现）问题，尤其是样本难得的问题