Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets

2006 ◽  
pp. 572-582 ◽  
Author(s):  
Jair Cervantes ◽  
Xiaoou Li ◽  
Wen Yu
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Clustering ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets ◽  
Support Vector Machine Classification

Nonlinear clustering-based support vector machine for large data sets

2008 ◽  
Vol 23 (4) ◽  
pp. 533-549 ◽  
Author(s):  
Yongqiao Wang ◽  
Xun Zhang ◽  
Souyang Wang ◽  
K.K. Lai
Keyword(s):  
Support Vector Machine ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets

Large data sets classification using convex–concave hull and support vector machine

2012 ◽  
Vol 17 (5) ◽  
pp. 793-804 ◽  
Author(s):  
Asdrúbal López Chau ◽  
Xiaoou Li ◽  
Wen Yu
Keyword(s):  
Support Vector Machine ◽  
Large Data ◽  
Large Data Sets ◽  
Support Vector ◽  
Data Sets

Scaling Fuzzy Models

2010 ◽  
pp. 31-53 ◽  
Author(s):  
Lawrence O. Hall ◽  
Dmitry B. Goldgof ◽  
Juana Canul-Reich ◽  
Prodip Hore ◽  
Weijian Cheng ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Approaches To Learning ◽  
Large Data ◽  
Medical Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Repositories ◽  
Fuzzy Models ◽  
Fuzzy Learning ◽  
Existing Data

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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