Databases Reference
In-Depth Information
3.5.1
Data Transformation Strategies Overview
In
data transformation
, the data are transformed or consolidated into forms appropriate
for mining. Strategies for data transformation include the following:
1.
Smoothing
, which works to remove noise from the data. Techniques include binning,
regression, and clustering.
2.
Attribute construction
(or
feature construction
), where new attributes are con-
structed and added from the given set of attributes to help the mining process.
3.
Aggregation
, where summary or aggregation operations are applied to the data. For
example, the daily sales data may be aggregated so as to compute monthly and annual
total amounts. This step is typically used in constructing a data cube for data analysis
at multiple abstraction levels.
4.
Normalization
, where the attribute data are scaled so as to fall within a smaller range,
such as 1.0 to 1.0, or 0.0 to 1.0.
5.
Discretization
, where the raw values of a numeric attribute (e.g.,
age
) are replaced by
interval labels (e.g., 0-10, 11-20, etc.) or conceptual labels (e.g.,
youth, adult
,
senior
).
The labels, in turn, can be recursively organized into higher-level concepts, resulting
in a
concept hierarchy
for the numeric attribute. Figure 3.12 shows a concept hierarchy
for the attribute
price
. More than one concept hierarchy can be defined for the same
attribute to accommodate the needs of various users.
6.
Concept hierarchy generation for nominal data
, where attributes such as
street
can
be generalized to higher-level concepts, like
city
or
country
. Many hierarchies for
nominal attributes are implicit within the database schema and can be automatically
defined at the schema definition level.
Recall that there is much overlap between the major data preprocessing tasks. The first
three of these strategies were discussed earlier in this chapter. Smoothing is a form of
($0...$1000]
($0...$200]
($200...$400]
($400...$600]
($600...$800]
($800...$1000]
($0...
$100]
($100...
$200]
($200...
$300]
($300...
$400]
($400...
$500]
($500...
$600]
($600...
$700]
($700...
$800]
($800...
$900]
($900...
$1000]
Figure 3.12
A concept hierarchy for the attribute
price
, where an interval
.
$
X
:::
$
Y
] denotes the range
from
$
X
(exclusive) to
$
Y
(inclusive).
