IBM 15 Manual Do Utilizador
Chapter
4
4
4
4
Understanding Data Mining
Data Mining Overview
Through a variety of techniques, data mining identifies nuggets of information in bodies of data.
Data mining extracts information in such a way that it can be used in areas such as decision
support, prediction, forecasts, and estimation. Data is often voluminous but of low value and with
little direct usefulness in its raw form. It is the hidden information in the data that has value.
Data mining extracts information in such a way that it can be used in areas such as decision
support, prediction, forecasts, and estimation. Data is often voluminous but of low value and with
little direct usefulness in its raw form. It is the hidden information in the data that has value.
In data mining, success comes from combining your (or your expert’s) knowledge of the
data with advanced, active analysis techniques in which the computer identifies the underlying
relationships and features in the data. The process of data mining generates models from historical
data that are later used for predictions, pattern detection, and more. The technique for building
these models is called machine learning or modeling.
relationships and features in the data. The process of data mining generates models from historical
data that are later used for predictions, pattern detection, and more. The technique for building
these models is called machine learning or modeling.
Modeling Techniques
IBM® SPSS® Modeler includes a number of machine-learning and modeling technologies, which
can be roughly grouped according to the types of problems they are intended to solve.
can be roughly grouped according to the types of problems they are intended to solve.
Predictive modeling methods include decision trees, neural networks, and statistical models.
Clustering models focus on identifying groups of similar records and labeling the records
according to the group to which they belong. Clustering methods include Kohonen, k-means,
and TwoStep.
and TwoStep.
Association rules associate a particular conclusion (such as the purchase of a particular
product) with a set of conditions (the purchase of several other products).
Screening models can be used to screen data to locate fields and records that are most likely to
be of interest in modeling and identify outliers that may not fit known patterns. Available
methods include feature selection and anomaly detection.
methods include feature selection and anomaly detection.
Data Manipulation and Discovery
SPSS Modeler also includes many facilities that let you apply your expertise to the data:
Data manipulation.
Constructs new data items derived from existing ones and breaks down the
data into meaningful subsets. Data from a variety of sources can be merged and filtered.
Browsing and visualization.
Displays aspects of the data using the Data Audit node to perform
an initial audit including graphs and statistics. Advanced visualization includes interactive
graphics, which can be exported for inclusion in project reports.
graphics, which can be exported for inclusion in project reports.
Statistics.
Confirms suspected relationships between variables in the data. Statistics from
IBM® SPSS® Statistics can also be used within SPSS Modeler.
Hypothesis testing.
Constructs models of how the data behaves and verifies these models.
© Copyright IBM Corporation 1994, 2012.
29