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Scaling Fuzzy Models

Scaling Fuzzy Models

Lawrence O. Hall, Dmitry B. Goldgof, Juana Canul-Reich, Prodip Hore, Weijian Cheng, Larry Shoemaker
Copyright: © 2010 |Pages: 23
ISBN13: 9781605668581|ISBN10: 1605668583|ISBN13 Softcover: 9781616924478|EISBN13: 9781605668598
DOI: 10.4018/978-1-60566-858-1.ch002
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MLA

Hall, Lawrence O., et al. "Scaling Fuzzy Models." Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, IGI Global, 2010, pp. 31-53. https://doi.org/10.4018/978-1-60566-858-1.ch002

APA

Hall, L. O., Goldgof, D. B., Canul-Reich, J., Hore, P., Cheng, W., & Shoemaker, L. (2010). Scaling Fuzzy Models. In A. Laurent & M. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design (pp. 31-53). IGI Global. https://doi.org/10.4018/978-1-60566-858-1.ch002

Chicago

Hall, Lawrence O., et al. "Scaling Fuzzy Models." In Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, edited by Anne Laurent and Marie-Jeanne Lesot, 31-53. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-858-1.ch002

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Abstract

This chapter examines how to scale algorithms which learn fuzzy models from the increasing amounts of labeled or unlabeled data that are becoming available. Large data repositories are increasingly available, such as records of network transmissions, customer transactions, medical data, and so on. A question arises about how to utilize the data effectively for both supervised and unsupervised fuzzy learning. This chapter will focus on ensemble approaches to learning fuzzy models for large data sets which may be labeled or unlabeled. Further, the authors examine ways of scaling fuzzy clustering to extremely large data sets. Examples from existing data repositories, some quite large, will be given to show the approaches discussed here are effective.

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