DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM model

Huber, Steffen; Wiemer, Hajo; Schneider, Dorothea; Ihlenfeldt, Steffen

doi:10.1016/j.procir.2019.02.106

Cited by 179 publications

(106 citation statements)

References 4 publications

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“…To pursuit the goal defined for this study we have considered as possible methodologies CRISP-DM-CRoss Industry Standard Process for Data Mining, DMME-Data Mining Methodology for Engineering Applications or SEMMA-Sample, Explore, Modify, Model, and Assess, the most common approaches on data mining projects [23,24].…”

Section: Methodsmentioning

confidence: 99%

“…It's a popular methodology that has proven to increase success on data mining challenges, including in the medical area [25][26][27]. DMME it's an extension of the CRISP-DM methodology that includes the process of data acquisition [23].…”

Section: Methodsmentioning

confidence: 99%

“…SEMMA was developed by the SAS Institute and is more commonly used by SAS tools users as it is integrated into SAS tools such as Enterprise Miner [23].…”

Section: Methodsmentioning

confidence: 99%

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Child’s Target Height Prediction Evolution

2019

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This study is a contribution for the improvement of healthcare in children and in society generally. This study aims to predict children's height when they become adults, also known as "target height", to allow for a better growth assessment and more personalized healthcare. The existing literature describes some existing prediction methods, based on longitudinal population studies and statistical techniques, which with few information resources, are able to produce acceptable results. The challenge of this study is in using a new approach based on machine learning to forecast the target height for children and (eventually) improve the existing height prediction accuracy. The goals of the study were achieved. The extreme gradient boosting regression (XGB) and light gradient boosting machine regression (LightGBM) algorithms achieved considerably better results on the height prediction. The developed model can be usefully applied by pediatricians and other clinical professionals in growth assessment.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Child’s Target Height Prediction Evolution

2019

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“…To corroborate this view from data science experts, we also checked that CRISP-DM is still a very common methodology for data mining applications. For instance, just focussing on the past four years, we can find a large number of conventional studies applying or slightly adapting the CRISP-DM methodology to many different domains: healthcare [18], [19], [20], [21], signal processing [22], engineering [23], [24], education [25], [26], [27], [28], [29], logistics [30] production [31], [32], sensors and wearable applications [33], tourism [34], warfare [35], sports [36] and law [37]. However, things have evolved in the business application of data mining since CRISP-DM was published.…”

Section: Crisp-dm and Related Process Modelsmentioning

confidence: 99%

CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

Martínez-Plumed

Contreras-Ochando

Ferri

et al. 2021

IEEE Trans. Knowl. Data Eng.

239

101

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“…Inside contains the project phase where the focus is on their respective tasks and also the relationship between assignments one and the other. The relationship could exist between the tasks of data mining that depends on the purpose, background, and interests order, and most importantly the data [17]. Six phases of life cycle data mining project are shown in Figure 2.…”

Section: Crisp-dmmentioning

confidence: 99%

Medicine Inventory Grouping using Clustering Data Mining

Nugraha

2019

Indonesian J. of Inf. Syst.

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One of the main factors in health services is adequate medicine supplies. Puskesmas is one of the health services that is managed under the district and city health offices to serve patients every day. However, there are obstacles in the process of medicine supply at the Puskesmas. Puskesmas still uses medicine supply techniques manually by looking at the minimum medicine stock. In this way, many medicines are unused and even lacking. The application of data mining can be used as an analysis to determine the medicine supply according to the patient's needs. In the data mining method, the clustering algorithm is one of the most popular to use where the data belonging to the same cluster will be close to each other and will be far from the data about another cluster. For this reason, this study used clustering to classify types of medicines based on the number of medicine uses and requests. The results are obtained in the form of information on the type of medicine with rapid use and model of m with extended usage every month taken from three years of data. Also, information on the types of medicines from the clustering process can be used to improve better patient service.

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DMME: Data mining methodology for engineering applications – a holistic extension to the CRISP-DM model

Cited by 179 publications

References 4 publications

Child’s Target Height Prediction Evolution

Child’s Target Height Prediction Evolution

CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

Medicine Inventory Grouping using Clustering Data Mining

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