Data Cleaning

Introduction: Real-world data tend to be incomplete, noisy, and inconsistent. Data cleaning (or data cleansing) routines attempt to fill in missing values, smooth out noise while identifying outliers, and correct inconsistencies in the data. In this section, you will study basic methods for data cleaning.

Missing Values

Imagine that you need to analyze All Electronics sales and customer data. You note that many tuples have no recorded value for several attributes, such as customer income. How can you go about filling in the missing values for this attribute? Let’s look at the following methods:

Ignore the tuple:This is usually done when the class label is missing (assuming the mining task involves classification). 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.

1.  Fill in the missing value manually: In general, this approach is time-consuming and may not be feasible given a large data set with many missing values.

2.  Use a global constant to fill in the missing value:Replace all missing attribute values by the same constant, such as a label like “Unknown” or -∞. If missing values are replaced by, say, “Unknown,” then the mining program may mistakenly think that they form an interesting concept, since they all have a value in common—that of “Unknown.” Hence, although this method is simple, it is not foolproof.

3.  Use the attribute mean to fill in the missing value:For example, suppose that the average income of All Electronics customers is $56,000. Use this value to replace the missing value for income.

4.  Use the attribute mean for all samples belonging to the same class as the given tuple: For example, if classifying customers according to credit risk, replace the missing value with the average income value for customers in the same credit risk category as that of the given tuple.

5. Use the most probable value to fill in the missing value: This may be determined with regression, inference-based tools using a Bayesian formalism, or decision tree induction. For example, using the other customer attributes in your data set, you may construct a decision tree to predict the missing values for income. Decision trees, regression, and Bayesian inference are described in detail in Chapter 6.

Methods 3 to 6 bias the data. The filled-in value may not be correct. Method 6, however, is a popular strategy. In comparison to the other methods, it uses the most information from the present data to predict missing values. By considering the values of the other attributes in its estimation of the missing value for income, there is a greater chance that the relationships between income and the other attributes are preserved. It is important to note that, in some cases, a missing value may not imply an error in the data! For example, when applying for a credit card, candidates may be asked to supply their driver’s license number. Candidates who do not have a driver’s license may naturally leave this field blank. Forms should allow respondents to specify values such as “not applicable”. Software routines may also be used to uncover other null values, such as “don’t know”, “?”, or “none”. Ideally, each attribute should have one or more rules regarding the null condition. The rules may specify whether or not nulls are allowed, and/or how such values should be handled or transformed. Fields may also be intentionally left blank if they are to be provided in a later step of the business process. Hence, although we can try our best to clean the data after it is seized, good design of databases and of data entry procedures should help minimize the number of missing values or errors in the first place.