Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering

Prasad, M; Li, DL; Lin, CT; Prakash, S; Singh, J; Joshi, S

Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering

Prasad, M

Li, DL Lin, CT

Prakash, S Singh, J Joshi, S

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Publication Type:: Journal Article
Citation:: IAENG International Journal of Computer Science, 2015, 42 (4), pp. 404 - 411
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Li, DL	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Prakash, S	en_US
dc.contributor.author	Singh, J	en_US
dc.contributor.author	Joshi, S	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	IAENG International Journal of Computer Science, 2015, 42 (4), pp. 404 - 411	en_US
dc.identifier.issn	1819-656X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116463
dc.description.abstract	In this paper a collaborative fuzzy c-means (CFCM) is used to generate fuzzy rules for fuzzy inference systems to evaluate the time series model. CFCM helps system to integrate two or more different datasets having similar features which are collected at the different environment with the different time period and it integrates these datasets together in order to visualize some common patterns among the datasets. In order to do any mode of integration between datasets, there is a necessity to define the common features between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling.	en_US
dc.relation.ispartof	IAENG International Journal of Computer Science	en_US
dc.title	Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	42	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	42	en_US

Abstract:

In this paper a collaborative fuzzy c-means (CFCM) is used to generate fuzzy rules for fuzzy inference systems to evaluate the time series model. CFCM helps system to integrate two or more different datasets having similar features which are collected at the different environment with the different time period and it integrates these datasets together in order to visualize some common patterns among the datasets. In order to do any mode of integration between datasets, there is a necessity to define the common features between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/116463