Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

Anca Avram, Oliviu Matei, Camelia Pintea and Carmen Anton
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Anca Avram: Electric, Electronic and Computer Engineering Department, Technical University of Cluj-Napoca, North University Center of Baia Mare, 400114 Cluj-Napoca, Romania
Oliviu Matei: Electric, Electronic and Computer Engineering Department, Technical University of Cluj-Napoca, North University Center of Baia Mare, 400114 Cluj-Napoca, Romania
Camelia Pintea: Department of Mathematics and Informatics, Technical University of Cluj-Napoca, North University Center of Baia Mare, 400114 Cluj-Napoca, Romania
Carmen Anton: Electric, Electronic and Computer Engineering Department, Technical University of Cluj-Napoca, North University Center of Baia Mare, 400114 Cluj-Napoca, Romania

Mathematics, 2020, vol. 8, issue 5, 1-19

Abstract: The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique—CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms—k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform.

Keywords: context-aware data mining; collaborative data mining; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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