The second method differs in implementation for each algorithm, but generally defines how missing values are processed and counted in models that permit null values. The first method controls the handling of nulls at the level of the mining structure. Therefore, Analysis Services provides two distinctly different mechanisms for managing and calculating missing values. By doing so, you can ensure that models are balanced and do not weight existing cases too heavily. Generally, Analysis Services treats missing values as informative and adjusts the probabilities to incorporate the missing values into its calculations. For example, a missing value for the date in a list of invoices has a meaning substantially different from the lack of a date in column that indicates an employee hire date.
The meaning of the missing values depends largely on context. However, there are many data mining scenarios in which missing values provide important information. It could be that the person who entered the data did not know the right value, or did not care if a field was not filled in. Perhaps the field was not applicable, the event did not happen, or the data was not available. Definition of Missing Values in Data MiningĪ missing value can signify a number of different things. This section explains what missing values are, and describes the features provided in Analysis Services to work with missing values when building data mining structures and mining models. Handling missing values correctly is an important part of effective modeling. To learn more, see Analysis Services backward compatibility. Documentation is not updated for deprecated features. Data mining is deprecated in SQL Server Analysis Services 2017 and will be discontinued in a future release.
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