• Corpus ID: 59777418

CRISP-DM 1.0: Step-by-step data mining guide

@inproceedings{Chapman2000CRISPDM1S,
  title={CRISP-DM 1.0: Step-by-step data mining guide},
  author={Peter Chapman and Janet Clinton and Randy Kerber and Tom Khabaza and Thomas P. Reinartz and Colin Shearer and Richard Wirth},
  year={2000}
}
This document describes the CRISP-DM process model, including an introduction to the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide and the CRISP-DM reports, as well as an appendix with additional useful and related information. This document and information herein, are the exclusive property of the partners of the CRISP-DM All trademarks and service marks mentioned in this document are marks of their respective owners and are as such acknowledged by the members of… 

Figures and Tables from this paper

Adapting the CRISP-DM Data Mining Process: A Case Study in the Financial Services Domain
TLDR
A case study in a financial services organization is reported on, aimed at identifying perceived gaps in the CRISPDM process and characterizing how CRISP-DM is adapted to address these gaps.
CMIN — a CRISP-DM-based case tool for supporting data mining projects
TLDR
CMIN’s capacity for easily and intuitively monitoring projects is highlighted, as is the manner in which CMIN allows a user to increase knowledge regarding using CRISP-DM or any other process defined in the CASE tool through the help and information presented in each step.
Cost Drivers of a Parametric Cost Estimation Model for Data Mining Projects (DMCOMO)
TLDR
The cost driver of a parametric estimation method for Data Mining projects is proposed, which is a method to estimate the global cost of a generic Data Mining project.
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories
TLDR
It is argued that if the project is goal-directed and process-driven the process model view still largely holds, and when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for.
From the Business Decision Modeling to the Use Case Modeling in Data Mining Projects
TLDR
This paper proposes a methodological approach to guide the development of a business model of the decision-making process within an organization based on the business decision- making model represented in i* notation.
Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector
TLDR
It is concluded that there is a critical need for a novel model to be developed in order to cater to the SMEs' prerequisite, especially so in the transportation sector context.
Knowledge Discovery : Enhancing Data Mining and Decision Support Integration
TLDR
A comprehensive survey of existing research in integrating data mining and decision supports and techniques in improving the performance of data mining algorithms and decision support is presented.
Using data mining for bank direct marketing: an application of the CRISP-DM methodology
The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Furthermore, economical pressures and competition has led marketing managers to invest on
Implementation of CRISP Methodology for ERP Systems
TLDR
Improve the usability and understandability of process mining techniques, by implementing CRISP-DM methodology for their application in ERP contexts, detailed in terms of specific implementation tools and step by step coordination.
Historical Data Analysis through Data Mining From an Outsourcing Perspective: The Three-Phases Model
TLDR
The Three-phases method, which consists of data retrieval, data mining and results implementation within an organization, is proposed, and the model is validated through expert interviews and an extensive case study at a large Dutch staffing company, substantiating the authors’ claim that it accurately describes the data mining process from an outsourcing perspective.
...
...