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Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. In this work, Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in...
Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of...
This paper proposes a new classification method based on association rule mining. This association rule-based classifier is experimented on a real dataset; a database of medical images from MIAS database. The proposed system employs Ant-Miner metaheuristic algorithm for extracting knowledge in the form of decision rules using texture features extracted with the help of co-occurrence matrices. These...
Genetic algorithm (GA) and Ant colony optimization (ACO) algorithm are proposed for feature selection, and their performance is compared. The spatial gray level dependence method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer backpropagation network hybrid with ant colony optimization (BPN-ACO) for classification. And the receiver operating characteristic (ROC)...
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