Database Reference
In-Depth Information
you've downloaded the Chapter 3 data set, you're ready to begin learning how to handle and
prepare data for mining in RapidMiner.
PREPARING RAPIDMINER, IMPORTING DATA, AND
HANDLING MISSING DATA
Our first task in data preparation is to handle missing data, however, because this will be our first
time using RapidMiner, the first few steps will involve getting RapidMiner set up. We'll then move
straight into handling missing data. Missing data are data that do not exist in a data set. As you
can see in Figure 3-5, missing data is not the same as zero or some other value. It is blank, and the
value is unknown. Missing data are also sometimes known in the database world as null .
Depending on your objective in data mining, you may choose to leave missing data as they are, or
you may wish to replace missing data with some other value.
Figure 3-5: Some missing data within the survey data set.
The creation of views is one way that data from a relational database can be collated and organized
in preparation for data mining activities. In this example, our database view has missing data in a
number of its attributes. Black arrows indicate a couple of these attributes in Figure 3-5 above. In
some instances, missing data are not a problem, they are expected. For example, in the Other
Social Network attribute, it is entirely possible that the survey respondent did not indicate that they
use social networking sites other than the ones proscribed in the survey. Thus, missing data are
probably accurate and acceptable. On the other hand, in the Online Gaming attribute, there are
answers of either 'Y' or 'N', indicating that the respondent either does, or does not participate in
online gaming. But what do the missing, or null values in this attribute indicate? It is unknown to
us. For the purposes of data mining, there are a number of options available for handling missing
data.
To learn about handling missing data in RapidMiner, follow the steps below to connect to your
data set and begin modifying it:
 
 
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