在数据挖掘的过程中,我们可能会经常遇到一些偏离于预测趋势之外的数据,通常我们称之为异常值。
我们通常将这样的一些数据的出现归为误差,各种各样的误差,也许是仪器、也许是测量人员、也许是其他的一些什么原因……
基于这样的原因,我们更多的时候将异常值弃之不理,可是这样的处理真的正确吗?
基本的想法:
1. 丢弃:
如果主要考虑的是统计学上的分布情况,我们更为在意的是群体的统计学的特点,那么,这少数数据表现出来的异常一般认为是可以丢弃的;
2. 重现:
如果说这样的数据出现一次,我们将这归为误差,那么重现无疑是最好的证明方式,不论是证实还是证伪;
3. 找到原因:
一切现象的出现都是有其原因的,所不同的只是原因是什么。如果能够找到出现异常的原因,也许比目标的结论更有意义。
所以处理的一般建议:
->N->暂时丢弃(但记录在案,这很重要)
重现 ->Y-> 找到原因 -> Y->成功
-> N ->交给合适的人
(也许你没有能力找到原因,但是你已经发现了她的踪影)
关于outlier的简要介绍:
更多极可能有用的书:
1.Geographic data mining and knowledge discovery [Miller, Harvey J.]
2.Identification of outliers [Hawkins, Douglas M.]
3.Identification of outliers in large statistical data [Hadi]
4.Numerical computer methods. Part C [Johnson, Michael L., 1947-]
5.Outliers in statistical data [Barnett, Vic.1994]
一本也许有趣的书
Outliers: The Story of SuccessDetailsSynopsisCultural
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Malcolm Gladwell, here a public intellectual, is himself an Outlier.
Size |
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(11/30/2008)
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