CRoss Industry Standard Process
for Data Mining
Process Model
The current process model for data mining provides an overview of the life
cycle of a data mining project. It contains the corresponding phases of a
project, their respective tasks, and relationships between these tasks. At
this description level, it is not possible to identify all relationships. There
possibly exists relationships between all data mining tasks depending on
goals, background and interest of the user, and most importantly
depending on the data. An electronic copy of the CRISP-DM Version 1.0
Process Guide and User Manual is available free of charge. This contains
step-by-step directions, tasks and objectives for each phase of the Data
Mining Process. Download CRISP 1.0 Process and User Guide.
Figure: Phases of the CRISP-DM Process Model
The life cycle of a data mining project consists of six phases. The sequence
of the phases is not strict. Moving back and forth between different phases
is always required. It depends on the outcome of each phase which phase,
or which particular task of a phase, that has to be performed next. The
arrows indicate the most important and frequent dependencies between
phases.
The outer circle in the figure symbolizes the cyclic nature of data mining
itself. A data mining process continues after a solution has been deployed.
The lessons learned during the process can trigger new, often more
focused business questions. Subsequent data mining processes will benefit
from the experiences of previous ones.
Below follows a brief outline of the phases:
Business Understanding
This initial phase focuses on understanding the project objectives and
requirements from a business perspective, and 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 needed.
Evaluation
At this stage in the project you have built a model (or models) that
appears 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. 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. In many cases it will be the customer, not
the data analyst, who will carry 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 will need to be carried out in
order to actually make use of the created models.
