Title :
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
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;
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
