数据挖掘中的数据缺失处理

The Treatment of Missing Data in Data Mining

数据缺失是数据挖掘中不可避免和必须解决的问题,目前常见的缺失值填充方法主要有统计学方法和机器学习方法。KNNI是一种数据挖掘领域常用的非常简单的同时准确率较高的缺失值填补算法;SNI填补算法则克服了KNNI在选择k最近邻时存在的某种偏好的缺点,更好地解决了数据缺失问题。

Missing data is an unavoidable and critical problem which must be resolved in data mining. The current common missing data imputation methods include statistical analysis methods and machine learning. KNNI is a very simple and highly accurate missing value imputation method in data mining field. SNI algorithm will solve the problem that the KNNI algorithm is often biased in choosing the k nearest neighbors of missing data and better solve the problem of missing data.