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2

Getting to Know Your Data

It’s tempting to jump straight into mining, but first, we need to get the data ready. This involves
having a closer look at attributes and data values. Real-world data are typically noisy,
enormous in volume (often several gigabytes or more), and may originate from a hodge-
podge of heterogenous sources. This chapter is about getting familiar with your data.
Knowledge about your data is useful for data preprocessing (see Chapter 3), the first
major task of the data mining process. You will want to know the following: What are
the types of attributes or fields that make up your data? What kind of values does each
attribute have? Which attributes are discrete, and which are continuous-valued? What
do the data look like? How are the values distributed? Are there ways we can visualize
the data to get a better sense of it all? Can we spot any outliers? Can we measure the
similarity of some data objects with respect to others? Gaining such insight into the data
will help with the subsequent analysis.
“So what can we learn about our data that’s helpful in data preprocessing?” We begin
in Section 2.1 by studying the various attribute types. These include nominal attributes,
binary attributes, ordinal attributes, and numeric attributes. Basic statistical descriptions
can be used to learn more about each attribute’s values, as described in Section 2.2.
Given a temperature attribute, for example, we can determine its mean (average value),
median (middle value), and mode (most common value). These are measures of
central tendency, which give us an idea of the “middle” or center of distribution.
Knowing such basic statistics regarding each attribute makes it easier to fill in missing
values, smooth noisy values, and spot outliers during data preprocessing. Knowledge of
the attributes and attribute values can also help in fixing inconsistencies incurred dur-
ing data integration. Plotting the measures of central tendency shows us if the data are
symmetric or skewed. Quantile plots, histograms, and scatter plots are other graphic dis-
plays of basic statistical descriptions. These can all be useful during data preprocessing
and can provide insight into areas for mining.
The field of data visualization provides many additional techniques for viewing data
through graphical means. These can help identify relations, trends, and biases “hidden”
in unstructured data sets. Techniques may be as simple as scatter-plot matrices (where

Data Mining: Concepts and Techniques 39
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2012 Elsevier Inc. All rights reserved.