We have observed various types of data stores and database systems on which
data mining can be performed. Let us now examine the kinds of data patterns that
can be mined. Data mining functionalities are used to specify the kind of patterns
to be found in data mining tasks. In general, data mining tasks can be classifi ed into
two categories: descriptive and predictive. Descriptive mining tasks characterize
the general properties of the data in the database. Predictive mining tasks perform
inference on the current data in order to make predictions.
In some cases, users may have no idea of which kinds of patterns in their data may
be interesting, and hence may like to search for several diff erent kinds of patterns in
parallel. Thus it is important to have a data mining system that can mine multiple
kinds of patterns to accommodate diff erent user expectations or applications.
Furthermore, data mining systems should be able to discover patterns at various
granularities (i.e., diff erent levels of abstraction). To encourage interactive and
exploratory mining, users should be able to easily "play" with the output patterns,
such as by mouse clicking. Operations that can be specifi ed by simple mouse clicks
include adding or dropping a dimension (or an attribute), swapping rows and columns
(pivoting, or axis rotation), changing dimension representations (e.g., from a 3-D cube
to a sequence of 2-D cross tabulations, or crosstabs), or using OLAP roll-up or drill
down operations along dimensions. Such operations allow data patterns to be
expressed from diff erent angles of view and at multiple levels of abstraction.
Data mining systems should also allow users to specify hints to guide or focus the
search for interesting patterns. Since some patterns may not hold for all of the data in
the database, a measure of certainty or "trustworthiness" is usually associated with
each discovered pattern.
Data mining functionalities, and the kinds of patterns are,
1. Concept/class description
2. Association analysis
3. Classi cation and prediction
4. Clustering analysis
5. Evolution and deviation analysis
data mining can be performed. Let us now examine the kinds of data patterns that
can be mined. Data mining functionalities are used to specify the kind of patterns
to be found in data mining tasks. In general, data mining tasks can be classifi ed into
two categories: descriptive and predictive. Descriptive mining tasks characterize
the general properties of the data in the database. Predictive mining tasks perform
inference on the current data in order to make predictions.
In some cases, users may have no idea of which kinds of patterns in their data may
be interesting, and hence may like to search for several diff erent kinds of patterns in
parallel. Thus it is important to have a data mining system that can mine multiple
kinds of patterns to accommodate diff erent user expectations or applications.
Furthermore, data mining systems should be able to discover patterns at various
granularities (i.e., diff erent levels of abstraction). To encourage interactive and
exploratory mining, users should be able to easily "play" with the output patterns,
such as by mouse clicking. Operations that can be specifi ed by simple mouse clicks
include adding or dropping a dimension (or an attribute), swapping rows and columns
(pivoting, or axis rotation), changing dimension representations (e.g., from a 3-D cube
to a sequence of 2-D cross tabulations, or crosstabs), or using OLAP roll-up or drill
down operations along dimensions. Such operations allow data patterns to be
expressed from diff erent angles of view and at multiple levels of abstraction.
Data mining systems should also allow users to specify hints to guide or focus the
search for interesting patterns. Since some patterns may not hold for all of the data in
the database, a measure of certainty or "trustworthiness" is usually associated with
each discovered pattern.
Data mining functionalities, and the kinds of patterns are,
1. Concept/class description
2. Association analysis
3. Classi cation and prediction
4. Clustering analysis
5. Evolution and deviation analysis
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