CRISP Algorithm

CRISP Algorithm is essentially one of the different analytical methods for the data mining process,This term is composed of the CRoss Industry Standard Process for Data Mining.
CRISP algorithm steps
* Business Understanding
* Data Understanding
* Data Preparation
* Modeling
* Evaluation
* Deployment
Business Understanding: Includes compilation of required items and discussions with senior executives for setting goals.
Data Understanding: Look closely and review access to data for the data mining process, which includes collecting, describing, discovering and modifying data quality.
Data Preparation: This step is one of the most important and time-consuming data mining sectors, including selection, cleanup, structuring, and data integration.
Modeling: Data are now ready for the data mining process, and the results show solutions to the commercial problem, modeling choice techniques, designing a test design, constructing models, and evaluating the model of this stage.
Evaluation: At this stage, the evaluated results, the process of carrying out the review work and the subsequent stages are carried out.
Deployment: The results are developed and used to improve the performance of the organization.
This translated description of one of the posts on the "" website (July 10, 2017) was reviewed July 17, 2017
Problems with the algorithm
Clustering methods can not solve all the requirements of a problem simultaneously and simultaneously. In large data due to the time complexity problem, the algorithm is not applicable to each data, and also in data that has many properties, there is the possibility of occurring results with different interpretations.
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