Unsupervised partitioning of numerical attributes using fuzzy sets

Author

Popescu, Bogdan ; Popescu, Andreea ; Brezovan, Marius ; Ganea, Eugen

Author_Institution

Fac. of Autom., Comput. & Electron., Univ. of Craiova, Craiova, Romania

fYear

2012

fDate

9-12 Sept. 2012

Firstpage

751

Lastpage

754

Abstract

The current paper presents an enhanced partitioning mechanism for numerical data. The efficiency of our method will be illustrated through a solid set of tests that have been performed. We have planned this partitioning phase as an initial step in a more complex algorithm to be further studied and implemented. The final goal is to use it for future decision making in automatic image annotation. Fuzzy Sets theory has been used as a base for our clustering algorithm and partitioning. We included this mechanism as a component of a framework we developed for image processing, more exactly for the image segmentation evaluation model we are building.

Keywords

data mining; decision making; fuzzy set theory; image segmentation; numerical analysis; pattern clustering; unsupervised learning; automatic image annotation; clustering algorithm; clustering partitioning; data mining; decision making; fuzzy sets theory; image processing; image segmentation evaluation model; numerical attributes; numerical data; partitioning mechanism; unsupervised partitioning; Association rules; Clustering algorithms; Fuzzy sets; Image segmentation; Indexes; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on

Conference_Location

Wroclaw

Print_ISBN

978-1-4673-0708-6

Electronic_ISBN

978-83-60810-51-4

Type

conf

Filename

6354414