CRISP-NET: Integration of the CRISP-DM Model with Network Analysis

To carry out data analysis, it is necessary to implement a model that guides the process in an orderly and sequential manner, with the aim of maintaining control over software development and its documentation. One of the most widely used tools in the field of data analysis is the Cross-Industry Standard Process for Data Mining (CRISP-DM), which serves as a reference framework for data mining, allowing the identification of patterns and, based on them, supporting informed decision-making. Another tool used for pattern identification and the study of relationships within systems is network analysis (NA), which makes it possible to explore how different components are interconnected. The integration of these tools can be justified and developed under the principles of Situational Method Engineering (SME), which allows for the adaptation and customization of existing methods according to the specific needs of a problem or context. Through SME, it is possible to determine which components of CRISP-DM need to be adjusted to efficiently incorporate NA, ensuring that this integration aligns with the project’s objectives in a structured and effective manner. The proposed methodological process was applied in a real working group, which allowed its functionality to be validated, each phase to be documented, and concrete outputs to be generated, demonstrating its usefulness for the development of analytical projects.

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Publication Details

Journal
Machine Learning and Knowledge Extraction
Published
2025-09-16
DOI
https://doi.org/10.3390/make7030101
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

CRISP-NET: Integration of the CRISP-DM Model with Network Analysis

Richard David Evans, Eduardo Ahumada‐Tello, Óscar Omar Ovalle Osuna, Héctor Alejandro Acuña-Cid et al.
Machine Learning and Knowledge Extraction
Complex Network Analysis Techniques
article

CRISP-NET: Integration of the CRISP-DM Model with Network Analysis

Richard David Evans, Eduardo Ahumada‐Tello, Óscar Omar Ovalle Osuna, Héctor Alejandro Acuña-Cid, Julia Elena Hernández-Ríos, Miriam Alondra Zambrano-Soto
article en

Abstract

To carry out data analysis, it is necessary to implement a model that guides the process in an orderly and sequential manner, with the aim of maintaining control over software development and its documentation. One of the most widely used tools in the field of data analysis is the Cross-Industry Standard Process for Data Mining (CRISP-DM), which serves as a reference framework for data mining, allowing the identification of patterns and, based on them, supporting informed decision-making. Another tool used for pattern identification and the study of relationships within systems is network analysis (NA), which makes it possible to explore how different components are interconnected. The integration of these tools can be justified and developed under the principles of Situational Method Engineering (SME), which allows for the adaptation and customization of existing methods according to the specific needs of a problem or context. Through SME, it is possible to determine which components of CRISP-DM need to be adjusted to efficiently incorporate NA, ensuring that this integration aligns with the project’s objectives in a structured and effective manner. The proposed methodological process was applied in a real working group, which allowed its functionality to be validated, each phase to be documented, and concrete outputs to be generated, demonstrating its usefulness for the development of analytical projects.

Machine Learning and Knowledge ExtractionVol. 7(3)
Dalhousie University (CA), Universidad Autónoma de Baja California (MX), Universidad Politécnica de Zacatecas (MX)
Dalhousie University
Openalex Percentile: Top 80%
Complex Network Analysis Techniques
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CRISP-NET: Integration of the CRISP-DM Model with Network Analysis — Richard David Evans, Eduardo Ahumada‐Tello, et al. · Machine Learning and Knowledge Extraction (2025) | TGRS Research Map | TGRS