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Introduction to Data Mining Read chapter Read first chapter

2016 | OriginalPaper | Chapter

13. Dealing with Large Volumes of Data

Author : Prof. Max Bramer

Published in: Principles of Data Mining

Publisher: Springer London

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Abstract

This chapter is concerned with issues relating to large volumes of data, in particular the ability of classification algorithms to scale up to be usable for such volumes.
Some of the ways in which a classification task could be distributed over a local area network of personal computers are described and a case study using an extended version of the Prism rule induction algorithm known as PMCRI is presented. Techniques for evaluating a distributed system of this kind are then illustrated.
The issue of streaming data is also considered, leading to a discussion of a classification algorithm that lends itself well to an incremental approach: the Naïve Bayes classifier.

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previous chapter Measuring the Performance of a Classifier
next chapter Ensemble Classification
Literature
[1]
[2]
Catlett, J. (1991). Megainduction: machine learning on very large databases. Sydney: University of Technology. MATH
[3]
Provost, F. (2000). Distributed data mining: scaling up and beyond. In H. Kargupta & P. Chan (Eds.), Advances in distributed data mining. San Mateo: Morgan Kaufmann.
[4]
Stahl, F., Bramer, M., & Adda, M. (2009). PMCRI: a parallel modular classification rule induction framework. In LNAI: Vol. 5632. Machine learning and data mining in pattern recognition (pp. 148–162). Berlin: Springer. CrossRef
[5]
Shafer, J. C., Agrawal, R., & Mehta, M. (1996). SPRINT: a scalable parallel classifier for data mining. In Twenty-second international conference on very large data bases.
[6]
Stahl, F. T., Bramer, M. A., & Adda, M. (2010). J-PMCRI: a methodology for inducing pre-pruned modular classification rules. In Artificial intelligence in theory and practice III (pp. 47–56). Berlin: Springer. CrossRef
[7]
Stahl, F., & Bramer, M. (2013). Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowledge based systems. Amsterdam: Elsevier.
[8]
Nolle, L., Wong, K. C. P., & Hopgood, A. (2002). DARBS: a distributed blackboard system. In M. A. Bramer, F. Coenen, & A. Preece (Eds.), Research and development in intelligent systems XVIII. Berlin: Springer.
[9]
Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: massive online analysis, a framework for stream classification and clustering. Journal of Machine Learning Research, 99, 1601–1604.
Metadata
Title
Dealing with Large Volumes of Data
Author
Prof. Max Bramer
Copyright Year
2016
Publisher
Springer London
Book
Principles of Data Mining

Print ISBN: 978-1-4471-7306-9

Electronic ISBN: 978-1-4471-7307-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-1-4471-7307-6_13