Adaptations of data mining methodologies: a systematic literature review

Plotnikova, Veronika; Dumas, Marlon; Milani, Fredrik

doi:10.7717/peerj-cs.267

Cited by 56 publications

(35 citation statements)

References 107 publications

(144 reference statements)

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“…For this, numerous process models exist. MARISCAL et al and PLOTNIKOVA et al provide an overview about existing process models and show that almost all identified approaches are based on two original models: the Cross Industry Standard Process for Data Mining (CRISP-DM) and the Knowledge Discovery in Databases model (KDD) [33,34]. Therefore, these two models are subsequently described in more detail.…”

Section: Model Constructionmentioning

confidence: 99%

A Reference Process Model for Usage Data-Driven Product Planning

Meyer¹,

Wiederkehr²,

Panzner³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage datadriven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice.

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Section: Model Constructionmentioning

confidence: 99%

A Reference Process Model for Usage Data-Driven Product Planning

Meyer¹,

Wiederkehr²,

Panzner³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Penelitian ini menggunakan tahapan Cross-Industry Standard Process Model for Data Mining (CRISP-DM) dalam penerapan algoritme k-NN dan Naïve Bayes dengan Backward Elimination pada klasifikasi kepuasan pelanggan. CRISP-DM bertujuan untuk memberikan pedoman kepada praktisi untuk melakukan penambangan data pada set data besar (Plotnikova et al, 2020). Adapun tahapannya dapat dilihat pada Gambar 1.…”

Section: Metodologi Penelitianunclassified

“…Biasanya, beberapa teknik digunakan untuk menangani masalah data mining yang serupa. Pada penelitian ini teknik klasifikasi dipilih untuk membandingkan penerapan algoritme k-NN dengan dan tanpa Backward Elimination, serta algoritme Naïve Bayes dengan dan tanpa Backward Elimination [17].…”

Section: Modellingunclassified

Optimasi Backward Elimination untuk Klasifikasi Kepuasan Pelanggan Menggunakan Algoritme k-nearest neighbor (k-NN) and Naive Bayes

Yunitasari

Hopipah

Mayasari

2021

TMJ

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Mempertahankan kepuasan pelanggan merupakan sebuah tantangan besar bagi perusahaan. Salah satu upaya yang dapat dilakukan adalah memberikan pelayanan terbaik terhadap pelanggan berdasarkan aspek yang paling berpengaruh. Pada penelitian ini dilakukan optimasi fitur Backward Elimination pada klasifikasi kepuasan pelanggan dengan algoritme k-NN dan Naïve Bayes. Penggunaan fitur Backward Elimination bertujuan meningkatkan akurasi dan mengurangi jumlah atribut yang kurang berpengaruh. Hasilnya, dapat diketahui bahwa pemodelan terbaik tanpa Backward Elimination adalah algoritme Naïve Bayes dengan akurasi 99.04% dan nilai AUC mencapai 1. Sedangkan penerapan Backward Elimination bekerja lebih optimal pada algoritme k-NN dengan peningkatan sebesar 33.74% menjadi 97.28% dengan AUC 0.996. Hal ini menunjukkan bahwa kinerja fitur Backward Elimination efektif dalam optimasi klasifikasi kepuasan pelanggan dan dapat mengurangi atribut yang kurang berpengaruh.

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“…It is one part of computer science that is bound up with disciplines such as statistics, probability, artificial intelligence, and machine learning, which has become a good research field that has gained a lot of attention due to its approach to data processing and information discovery [1], [2], [16]. Besides, it is defined as a set of rules, procedures, and algorithms to produce valuable insights, derive patterns, and draw connections from huge datasets [16]. It also incorporates advanced data retrieval, processing and simulation using several methods and techniques.…”

Section: B Data Miningmentioning

confidence: 99%

“…Several major data mining techniques have been developed and applied in pandemic outbreaks such as classification, clustering, and association rules. Classification is a method that allocates objects in a series to target groups whereby the same set of features is put into a class by this approach [16]. Naïve Bayes, Decision Tree, Artificial Neural Network, Support Vector Machine, Associative Classification and K-Nearest Neighbours are some of the methods of classification [2].…”

Section: B Data Miningmentioning

confidence: 99%

Data Mining Techniques for Pandemic Outbreak in Healthcare

Satar

Mohamed

Ali

2021

JOIV : Int. J. Inform. Visualization

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Pandemic outbreaks such as SARS-CoV, MERS-CoV and Covid-19 have attracted worldwide attention since these viruses have affected many countries and become a global public health issue. In 2019, Covid-19 was announced as a pandemic disease and categorized as a public health emergency globally. It is ranked as the sixth most serious pandemic internationally. This pandemic tracking and analysis require an appropriate method that gives better performance in terms of accuracy, precision and recall that defines its pattern since it involves huge and complicated datasets from the pandemic. Pattern identification is currently applied in many instances due to the rapid growth of data besides having the potential to generate a knowledge-rich environment which can help to significantly improve the quality of clinical decisions and identify the relationships between data items. Therefore, there is a need to review the techniques in data mining on the pandemic outbreak that focuses on healthcare. The goal of this study was to analyze the algorithms from the data mining method that had been implemented for pandemic outbreaks in past research such as SARS-CoV, MERS-CoV and Covid-19. The result shows that 2 main algorithms, namely Naïve Bayes and Decision Tree, from the classification method, are appropriate algorithms and give more than 90% accuracy in both the pandemic and healthcare. This will be further considered and investigated for future analysis on large datasets of Covid-19 which can help researchers and healthcare practitioners in controlling the infection of the coronavirus using the data mining technique discussed.

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Adaptations of data mining methodologies: a systematic literature review

Cited by 56 publications

References 107 publications

A Reference Process Model for Usage Data-Driven Product Planning

A Reference Process Model for Usage Data-Driven Product Planning

Optimasi Backward Elimination untuk Klasifikasi Kepuasan Pelanggan Menggunakan Algoritme k-nearest neighbor (k-NN) and Naive Bayes

Data Mining Techniques for Pandemic Outbreak in Healthcare

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