Databases Reference
In-Depth Information
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Regression
Generalized linear models
Analysis of variance
Mixed-effect models
Factor analysis
Discriminant analysis
Survival analysis
Statistical
Data Mining
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Data reduction
Data compression
Probability and statistical theory
Microeconomic view
Pattern discovery and inductive database
Foundations
of Data Mining
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Data visualization
Data mining result visualization
Data mining process visualization
Interactive visual data mining
Audio data mining
Visual and Audio
Data Mining
Figure 13.3
Other data mining methodologies.
typically multidimensional and possibly of various complex types. There are, however,
many well-established statistical techniques for data analysis, particularly for numeric
data. These techniques have been applied extensively to scientific data (e.g., data from
experiments in physics, engineering, manufacturing, psychology, and medicine), as well
as to data from economics and the social sciences. Some of these techniques, such as
principal components analysis (Chapter 3) and clustering (Chapters 10 and 11), have
already been addressed in this topic. A thorough discussion of major statistical methods
for data analysis is beyond the scope of this topic; however, several methods are men-
tioned here for the sake of completeness. Pointers to these techniques are provided in
the bibliographic notes (Section 13.8).
Regression
: In general, these methods are used to predict the value of a
response
(dependent) variable from one or more
predictor
(independent) variables, where the
variables are numeric. There are various forms of regression, such as linear, multi-
ple, weighted, polynomial, nonparametric, and robust (robust methods are useful
when errors fail to satisfy normalcy conditions or when the data contain significant
outliers).
Generalized linear models
: These models, and their generalization (
generalized addi-
tive models
), allow a
categorical
(nominal) response variable (or some transformation
