Question

1. In real-world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem.

Answer 1

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Real-world data tends to be incomplete, noisy, and inconsistent and an important task when preprocessing the data is to fill in missing values, smooth out noise and correct inconsistencies.

• Substitute a value such as mean.

When the percentage is large and also when it makes sense to do something to avoid bias modeling results, substituting a value (e.g. mean, median) is a commonly used way. But this method could cause bias distribution and variance. That’s where the following imputation methods come in.

• Predict missing values.

Depending on the type of the imputed variable (i.e. continuous, ordinal, nominal) and missing data pattern (i.e. monotone, non-monotone), below are a few commonly used models. If you plan to do it in SAS, there are SAS codes that you can write to identify the missing data pattern.

• Logistic Regression
• Discriminant Regression
• Markov Chain Monte Carlo (MCMC)

(a) Ignoring the tuple: This is usually done when the value is missing. This method is not very effective unless the tuple contains several attributes with missing values. It is especially poor when the percentage of missing values per attribute varies considerably.

(b) Manually filling in the missing value: In general, this approach is time-consuming and may not be a reasonable task for large data sets with many missing values, especially when the value to be filled

in is not easily determined.

(c) Using a global constant to fill in the missing value: Replace all missing attribute values by the same constant, such as a label like \Unknown," Hence, although this method is simple, it is not recommended.

(d) Using the attribute mean for quantitative (numeric) values or attribute mode for categorical (nominal) values: For example, suppose that the average income of AllElectronics customers is $28,000. Use this value to replace any missing values for income.

(e) Using the most probable value to fill in the missing value

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