document: Cleaning disguised missing data : a heuristic approach In some applications such as filling in a customer information form on the web , some missing values may not be explicitly represented as such , but instead appear as potentially valid data values . Such missing values are known as disguised missing data , which may impair the quality of data analysis severely , such as causing significant biases and misleading results in hypothesis tests , correlation analysis and regressions . The very limited previous studies on cleaning disguised missing data use outlier mining and distribution anomaly detection . They highly rely on domain background knowledge in specific applications and may not work well for the cases where the disguise values are inliers . To tackle the problem of cleaning disguised missing data , in this paper , we first model the distribution of disguised missing data , and propose the embedded unbiased sample heuristic . Then , we develop an effective and efficient method to identify the frequently used disguise values which capture the major body of the disguised missing data . Our method does not require any domain background knowledge to find the suspicious disguise values . We report an empirical evaluation using real data sets , which shows that our method is effective - the frequently used disguise values found by our method match the values identified by the domain experts nicely . Our method is also efficient and scalable for processing large data sets .
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