2002, 2004, 2007 and 2014 show that it was the leading methodology used by industry data miners who decided to respond to the survey. However, SAS Institute clearly states that SEMMA is not a data mining models pdf mining methodology, but rather a “logical organization of the functional tool set of SAS Enterprise Miner.
A review and critique of data mining process models in 2009 called the CRISP-DM the “de facto standard for developing data mining and knowledge discovery projects. Azevedo and Santos’ 2008 comparison of CRISP-DM and SEMMA. 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.
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. 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 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. 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. 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. 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 is useful to the customer.
In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. Even if the analyst deploys the model 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. CRISP-DM was conceived in 1996. OHRA was just starting to explore the potential use of data mining. Between 2006 and 2008 a CRISP-DM 2. 0 SIG was formed and there were discussions about updating the CRISP-DM process model. The current status of these efforts is not known.
IBM is the primary corporation that currently embraces the CRISP-DM process model. 453, February 2009, I-Tech, Vienna, Austria. 24, Cambridge University Press, New York, NY, USA doi: 10. This page was last edited on 30 October 2017, at 17:16. I hope you find it useful.