Oracle Analytics: Business Intelligence and Analytic Applications - The CIO's Guide to Oracle Products and Solutions - page 68

Data Mining

Automatic discovery is considered a close adjunct to data mining. Data
mining software amplifies our ability to navigate and analyze information
so that it can be rapidly turned from discrete and disconnected pieces of
data into real intelligence.
Data mining can not only help us in knowledge discovery, i.e., the identifi-
cation of new phenomena, but it is also useful in enhancing our understand-
ing of known phenomena. One of the key steps in data mining is pattern
recognition, namely, the discovery and characterization of patterns in
image and other high-dimensional data. A pattern is defined as an arrange-
ment or an ordering in which some organization of underlying structure
can be said to exist. Patterns in data are identified using measurable features
or attributes that have been extracted from the data, as shown in Figure 2.4.
Data mining is an interactive and iterative process involving data pre-
processing, search for patterns, knowledge evaluation, and possible refine-
ment of the process based on input from domain experts or feedback from
one of the steps. Wal-Mart is one company that champions data mining,
and it has profited handsomely from its use.
The preprocessing of the data is a time-consuming, but critical,
first step in the data-mining process. It is often domain and applica-
tion dependent; however, several techniques developed in the context
of one application or domain can be applied to other applications and

Preprocessed
Data

Transformed
Data Raw Data Target Data Patterns

Knowledge

Data Preprocessing Pattern Recognition Interpreting Results

Data Fusion
Sampling
Multi-resolution
analysis

De-noising
Object
identification
Feature
extraction
Normalization
Dimension
reduction

Classification
Clustering
Regression

Visualization
Validation

FIGURE 2.4
The process of data mining.