MBA (General) - IV Semester, Information Technology and E-Business, Unit 2.1
Data Mining Process
The steps in data mining standard process are as follows
Data Mining Process
The steps in data mining standard process are
as follows
Business
Understanding
This initial phase focuses on understanding the
project objectives and requirements from a business perspective, then
converting this knowledge into a data mining problem definition and a
preliminary plan designed to achieve the objectives.
Data
Understanding
The data understanding phase starts with an
initial data collection and proceeds with activities in order to get familiar
with the data, to identify data quality problems, to discover first insights
into the data or to detect interesting subsets to form hypotheses for hidden
information.
Data
Preparation
The data preparation phase covers all
activities to construct the final dataset (data that will be fed into the
modeling tool(s)) from the initial raw data. Data preparation tasks are likely
to be performed multiple times and not in any prescribed order. Tasks include
table, record and attribute selection as well as transformation and cleaning of
data for modeling tools.
Modeling
In this phase, various modeling techniques are
selected and applied and their parameters are calibrated to optimal values.
Typically, there are several techniques for the same data mining problem type.
Some techniques have specific requirements on the form of data. Therefore,
stepping back to the data preparation phase is often necessary.
Evaluation
At this stage in the project you have built a
model (or models) that appear to have high quality from a data analysis
perspective. Before proceeding to final deployment of the model, it is
important to more thoroughly evaluate the model and review the steps executed
to construct the model to be certain it properly achieves the business
objectives.
A key objective is to determine if there is
some important business issue that has not been sufficiently considered. At the
end of this phase, a decision on the use of the data mining results should be
reached.
Deployment
Creation of the model is generally not the end
of the project. Even if the purpose of the model is to increase knowledge of
the data, the knowledge gained will need to be organized and presented in a way
that the customer can use it. It often involves applying “live”models within an
organization’s decision making processes, for example in real-time
personalization of Web pages or repeated scoring of marketing databases.
However, depending on the requirements, the
deployment phase can be as simple as generating a report or as complex as
implementing a repeatable data mining process across the enterprise. In many
cases it is the customer, not the data analyst, who carries out the deployment
steps. However, even if the analyst will not carry out the deployment effort it
is important for the customer to understand up front what actions need to be
carried out in order to actually make use of the created models.
