Search results
Results from the WOW.Com Content Network
The first version of the methodology was presented at the 4th CRISP-DM SIG Workshop in Brussels in March 1999, [5] and published as a step-by-step data mining guide later that year. [6] Between 2006 and 2008, a CRISP-DM 2.0 SIG was formed, and there were discussions about updating the CRISP-DM process model. [7]
The methodology explains how to build predictive statistical models for software reliability and robustness and shows how simulation and analysis techniques can be combined with structural design and architecture methods to effectively produce software and information systems at Six Sigma levels.
5 Inclusion of some CRISP-DM 2.0 material. ... 6 Standard Methodology for Analytical Models (SMAM) 1 comment. 7 Source link for "CRISP-DM 1.0 Step-by-step data mining ...
There have been some efforts to define standards for the data mining process, for example, the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006 but has stalled since.
SEMMA mainly focuses on the modeling tasks of data mining projects, leaving the business aspects out (unlike, e.g., CRISP-DM and its Business Understanding phase). Additionally, SEMMA is designed to help the users of the SAS Enterprise Miner software. Therefore, applying it outside Enterprise Miner may be ambiguous. [3]
The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore, the first step in the model is to describe the business process which ...
Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Donate
Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial ...