Approaches and Techniques of Distributed Data Mining : A Comprehensive Study

By U. S., Nandhini Dr.M.. 2017

4 citations recorded by the source corpus.

- Distributed Data Mining (DDM) has become one of the promising areas of Data Mining (DM). DDM evolved from DM from the urge to mine data from distributed sites. DM paved way for increased computational cost and privacy due to centralized data mining, whereas DDM paved way for decrease in computational cost as well as enhanced data privacy by distributing resources across distributed sites. Mining techniques framed for DM can’t be applied for DDM since mining DDM follows a different strategy compared to DM. DDM includes classifier based, agent based and privacy preserving based approaches. In this paper, DDM approaches and techniques is studied in detail.

Subfields: Information Systems

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1. Eugenio Martínez-Cámara
2. M. V. Luzón
3. Daniel Jiménez-López
4. José Antonio Ruiz-Millán
5. Gerardo González-Seco
6. G. Stipcich
7. Francisco Herrera
8. M. Veganzones

Citations in the corpus

• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, methodology): “g techniques adapted to distributed scenarios. Regarding distributed data preprocessing, we might take inspiration from different distributed preprocessing techniques that have already been developed [42]. However, most of these methods need to be adapted in order to respect data privacy. A distribu”