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

By P. Chapman. 2000

1,439 citations recorded by the source corpus.

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Citations in the corpus

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “It is also not clear why the comparison should be performed on a static dataset, because any formal process for defining knowledge from data [15, 16, 17] would require an iterative process, where one refines the data processing after interpreting the results.”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, methodology): “…A ﬁctional depiction of the “accuracy-interpretability trade-off," taken from the DARPA XAI BAA. for deﬁning knowledge from data [Fayyad et al., 1996, Chapman et al., 2000, Agrawal et al., 2012] would require an iterative process, where one reﬁnes the data processing after interpreting the results.”
Opening black box Data Mining models using Sensitivity Analysis (2011, background): “Such knowledge is important to check if the obtained model makes sense to the domain experts and if it is interesting (e.g. if it unveils potentially useful, interesting, or novel information) [1][3].”
Problem Formulation and Fairness (2019, methodology): “CRISP-DM and other practical guidance for data miners or data scientists [see: 6,26] tend to describe problem formulation mainly as part of a project’s first phase—an initial occasion for frank conversations between the managers who set strategic business goals, the technologists that manage an orga”
Data Mining for Web Personalization (2007, background): “However, to be able to take full advantage of the flexibility provided by the data, and to effectively use the discovered models in an automatic personalization system, the process of personalization must be viewed as an application of data mining requiring support for all the phases of a typical da”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, methodology): “In 1999 the first version of the CRossIndustry Standard Process for Data Mining, better known as CRISP-DM, was introduced [1].”
Applying Data Mining Techniques to e-Learning Problems (2007, background): “At its most detailed, it can be understood not just as a collection of data analysis methods, but as a data analysis process that encompasses anything from data understanding, pre-processing and modelling to process evaluation and implementation [16].”
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods (2019, neutral): “Notably, most reproducible supervised studies follow the Cross Industry Standard Process for Data Mining or some modification thereof.(13,14) Unsupervised ML methods involve agnostic aggregation of unlabeled data sets yielding groups or clusters of entities with shared similarities that may be unkno”
Using Meta-Learning to Support Data Mining (2004, background): “The process of knowledge discovery from databases (KDD) includes several steps (e.g., [17]), such as understanding the problem domain, selecting data sources, data cleaning and pre-processing, data reduction and projection, task selection, algorithm or model selection, model evaluation and…”
Identifying Anomalies in Parliamentary Expenditures of Brazilian Chamber of Deputies with Deep Autoencoders (2017, methodology): “This study used the well-known data mining reference model Cross Industry Standard Process for Data Mining (CRISP-DM) [6].”
Machine learning for science and society (2013, background): “…for many years, by many different scientists, and is captured in the knowledge discovery frameworks of KDD and CRISP-DM (Frawley et al. 1992; Chapman et al. 2000) and by others who view the machine learning component as one part of a much larger formalized system for discovering knowledge…”