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Published in:
2013 | OriginalPaper | Chapter
Scalable and High Performing Learning and Mining in Large-Scale Networked Environments: A State-of-the-art Survey
Authors : Evis Trandafili, Marenglen Biba
Published in: Transactions on Computational Collective Intelligence X
Publisher: Springer Berlin Heidelberg
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Scalability is a major issue in the application of machine learning and data mining to large-scale networked environments. While there has been important progress in the learnability of models for medium-sized datasets, there is still much challenge in facing large-scale systems. In particular, with the evolution of distributed and networked environments, the complexity of the learning and mining process has now grown due to the possibility to integrating more data in the learning process. This paper provides a survey on the state-of-the-art on the methods and algorithms to enhance scalability of machine learning and data mining for large-scale networked systems.