Databases Reference
In-Depth Information
valid, novel, potentially useful, and ultimately understandable patterns from
data” [3]. The function-oriented approaches put forth efforts on searching,
mining and utilizing different patterns embedded in various databases. A pat-
tern is an expression in a language that describes data, and has a representa-
tion simpler than the data. For example, frequent itemsets, association rules
and correlations, as well as clusters of data points, are common classes of pat-
terns. Such goal-driven approaches establish a close link between data mining
research and real world applications.
Depending on the data and their properties, one may consider different
data mining systems with different functionalities and for different purposes,
such as text mining, Web mining, sequential mining, and temporal data min-
ing. Under the function-oriented view, the objectives of data mining can be
divided into prediction and description. Prediction involves the use of some
variables to predict the values of some other variables, and description focuses
on patterns that describe the data [3].
The Theory-Oriented View
The theory-oriented approaches concentrate on the theoretical studies of data
mining, and its relationship to the other disciplines. Many models of data
mining have been proposed, critically investigated and examined from the
theory-oriented point of view [3, 11, 21, 26].
Conceptually, one can draw a correspondence between scientific research
by scientists and data mining by computers [25, 26]. More specifically, they
share the same goals and processes. It follows that any theory discovered
and used by scientists can be used by data mining systems. Thus, many fields
contribute to the theoretical study of data mining. They include statistics, ma-
chine learning, databases, pattern recognition, visualization, and many other.
There is also a need for the combination of existing theories. For example,
some efforts have been made to bring the rough sets theory, fuzzy logic, util-
ity and measurement theory, concept lattice and knowledge structure, and
other mathematical and logical models into the data mining models.
The Procedure/Process-Oriented View
From the procedure/process-oriented view, data mining deals with a “non-
trivial” process consisting of many steps, such as data selection, data pre-
processing, data transformation, pattern discovery, pattern evaluation, and
result explanations [3, 10, 26, 30]. Furthermore, it should be a dynamically
organized process.
Under the process-oriented view, data mining studies have been focused
on algorithms and methodologies for different processes, speeding up existing
algorithms, and evaluation of discovered knowledge.
