CRISP-DM le modèle de processus standard pour la datamining #Marketingthema

par marketingthema

CRISP-DM signifie Cross Industry Standard Process for Data Mining1. Il s’agit d’un Modèle de Processus de data mining qui décrit une approche communément utilisée par les experts en data mining pour résoudre les problèmes qui se posent à eux. Des sondages effectués en 2002, 2004, et 2007 montrent qu’il s’agit de la méthode principale utilisée par les data miners. 2,3,4.Cette méthode a été créée par un consortium formé des compagnies NCR, SPSS, et Daimler-Benz. Le processus définit une hiérarchie consistant de phases majeures, de taches générales, de taches spécialisées, et d’instances de processus5.

CRISP-DM breaks the process of data mining into six major phases.[8]

The sequence of the phases is not strict and moving back and forth between different phases is always required. The arrows in the process diagram indicate the most important and frequent dependencies between phases. The outer circle in the diagram 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 and subsequent data mining processes will benefit from the experiences of previous ones.

  • 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 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. 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 the actions which will need to be carried out in order to actually make use of the created models.
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