A Novel Texture Classification Procedure by using Association Rules

Para a percentagem elevada de lesões malignas contribui uma quantidade relevante de doentes referenciadas para a Clínica de Mama já com o diagnóstico de carcinoma da mama

The adopted methodology comprises into three major steps: feature extraction, feature selection and image classification using two classifiers, K-Nearest Neighbors

The most interesting one is that oscillating search algorithm which is used for feature selection provides the best optimal features and no where it is applied or used for

The ACDR algorithm consists of 5 main steps which are (1) finding association rules, (2) clustering target association rules and general association rules into two groups

In this paper we propose a novel method for unsupervised analysis of EO'1 ALI panchromatic data by using the Multilevel Pattern Histogram texture descrip' tors and LDA

The results indicate that the proposed business rules tool REBIT, developed by AGH team as the project co -funded by the European Union, is feasible as a complete knowledge

This algorithm can extract the minimal generators and build a structure partially ordered called Frequent minimal generators lattice in order to perform a v ertical

[1] Using the new data mining techniques it can be discovered new patterns that offer new path analysis, new association rules, and new classification and clusters rules.. As we