Change search
ReferencesLink to record
Permanent link

Direct link
Fuzzy Clustering Analysis
Blekinge Institute of Technology, School of Engineering.
Blekinge Institute of Technology, School of Engineering.
Blekinge Institute of Technology, School of Engineering.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.

Place, publisher, year, edition, pages
2010. , 63 p.
Keyword [en]
Fuzzy Clustering, Pattern Recognition
National Category
Mathematical Analysis Mathematics Probability Theory and Statistics
URN: urn:nbn:se:bth-2165Local ID: diva2:829433
Physics, Chemistry, Mathematics
Available from: 2015-04-22 Created: 2010-05-05 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(1303 kB)505 downloads
File information
File name FULLTEXT01.pdfFile size 1303 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
School of Engineering
Mathematical AnalysisMathematicsProbability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 505 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 394 hits
ReferencesLink to record
Permanent link

Direct link