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Data Mining
Techniques
Data mining can be utilized in either supervised or unsupervised investigations. In
a supervised investigation, users can pose a question and expect an answer that
can drive their decision making. An unsupervised investigation is a pure pattern
discovery exercise where patterns are allowed to emerge, and then considered for
applicability to business decisions.
Data mining is a general term that covers descriptive, diagnostic, and predictive
techniques:
• Descriptive: such as clustering make it easier to see the patterns in a set of
data, such as similarities between customers.
• Diagnostic: such as decision trees or segmentation can show why a
pattern exists, such as the characteristics of an organization's most
profitable customers.
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• Predictive: such as regression or neural networks can show how likely
something is to be true in the future, such as predicting the probability that
a particular claim is fraudulent.
In all cases it is important to consider the goal of the data mining exercise and to
be prepared for considerable effort in securing the right type, volume, and quality
of data with which to work.
.1 Requirements Elicitation
The goal and scope of data mining is established either in terms of decision
requirements for an important identified business decision, or in terms of a
functional area where relevant data will be mined for domain-specific pattern
discovery. This top-down versus a bottom-up mining strategy allows analysts to
pick the correct set of data mining techniques.
Formal decision modelling techniques (see Decision Modelling (p. 265)) are used
to define requirements for top-down data mining exercises. For bottom-up
pattern discovery exercises it is useful if the discovered insight can be placed on
existing decision models, allowing rapid use and deployment of the insight.
Data mining exercises are productive when managed as an agile environment.
They assist rapid iteration, confirmation, and deployment while providing project
controls.
.2 Data Preparation: Analytical Dataset
Data mining tools work on an analytical dataset. This is generally formed by
merging records from multiple tables or sources into a single, wide dataset.
Repeating groups are typically collapsed into multiple sets of fields. The data may
be physically extracted into an actual file or it may be a virtual file that is left in the
database or data warehouse so it can be analyzed. Analytical datasets are split
into a set to be used for analysis, a completely independent set for confirming
that the model developed works on data not used to develop it, and a validation
set for final confirmation. Data volumes can be very large, sometimes resulting in
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