By Dr. Matthew A North
In Data Mining for the loads, professor Matt North—a former danger analyst and database developer for eBay.com—uses easy examples, transparent motives and free, robust, easy-to-use software program to coach you the fundamentals of knowledge mining concepts which could assist you resolution a few of your hardest company questions.
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Extra resources for Data Mining for the Masses
41 Data Mining for the Masses Figure 3-21. Results perspective for the Chapter3 data set. 17) You can toggle between design and results perspectives using the two icons indicated by the black arrows in Figure 3-21. As you can see, there is a rich set of information in results perspective. In the meta data view, basic descriptive statistics are given. It is here that we can also get a sense for the number of observations that have missing values in each attribute of the data set. The columns in meta data view can be stretched to make their contents more readable.
These might also be considered to be inconsistent data, so an example in a later chapter will illustrate the handling of statistical outliers. Sometimes data scrubbing can become tedious, but it will ultimately affect the usefulness of data mining results, so these types of activities are important, and attention to detail is critical. ATTRIBUTE REDUCTION In many data sets, you will find that some attributes are simply irrelevant to answering a given question. In Chapter 4 we will discuss methods for evaluating correlation, or the strength of relationships between given attributes.
Click on the Import CSV File option. 36 Chapter 3: Data Preparation Figure 3-14. Locating the data set to import. 10) When the data import wizard opens, navigate to the folder where your data set is stored and select the file. In this example, only one file is visible: the Chapter 3 data set downloaded from the companion web site. Click Next. Figure 3-15. Configuring attribute separation. 37 Data Mining for the Masses 11) By default, RapidMiner looks for semicolons as attribute separators in our data.