Scaling Fuzzy Models
Document Type
Book Chapter
Publication Date
2010
Digital Object Identifier (DOI)
https://doi.org/10.4018/978-1-60566-858-1.ch002
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.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Scaling Fuzzy Models, in A. Laurent, & M. Lesot (Eds.), Scalable Fuzzy Algorithms for Data Management and Analysis: Methods and Design, IGI Global, p. 31-53
Scholar Commons Citation
Hall, Lawrence O.; Goldgof, Dmitry; Canul-Reich, Juana; Hore, Prodip; Cheng, Weijian; and Shoemaker, Larry, "Scaling Fuzzy Models" (2010). Computer Science and Engineering Faculty Publications. 135.
https://digitalcommons.usf.edu/esb_facpub/135
