CRISP-DM ( CRoss Industry Standard Process for Data Mining )

 'CRISP-DM' 은 가장 이해하기 쉽고 모든 영역에 적용 가능한 데이터마이닝 방법론이다. 얼마 전 면접질문 구상하던 중 가장 먼저 떠오른 것이 이 것이었다. 기본은 초보자에게는 쉽고 당연하지만, 막상 업무를 해 본 사람들에게는 피가되고 살이 된다. 발표자료의 목차를 정리하다가 문득, 정리해서 포스팅하고 싶어졌다. (발표자료 만들기가 싫은거지..=_=)

 

 제일 중요한 과정은 앞의 3단계이다. 비즈니스를 이해해야 마이닝의 목적이 생기고, 데이터를 이해해야 과연 쓸만한 데이터인가를 확인해서 마이닝 작업의 설계도를 그릴 수 있으며, 데이터 전처리는 결과의 품질을 좌우한다.

 

 

Figure: Phases of the CRISP-DM Process Model

 

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.

 

 

 

※ 참고 : http://www.crisp-dm.org/Process/index.htm