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Mining of high dimension data for mammogram image classification is highly challenging. Feature reduction using subset selection plays enormous significance in the field of image mining to reduce the complexity of image mining process. This paper aims at investigating an improved image mining technique to enhance the automatic and semi-automatic semantic image annotation of mammography images using multivariate filters, which is the Correlation-based Feature Selection (CFS). This feature selection method is then applied onto two association rules mining methods, the Apriori and a modified genetic association rule mining technique, the GARM, to classify mammography images into their pathological labels. The findings show that the classification accuracy is improved with the use of CFS in both Apriori and GARM mining techniques.

Feature extraction is an important task for designing an OCR for recognizing degraded documents. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Shape inconsistency among characters of the same structure is sometimes quite large because of the poor resources and environmental impact on the document images. Therefore, it is necessary to select features which can adapt to the shape variations irrespective of the distortions. Hence, in this paper, selection of appropriate standard structural features is taken as the primary task for various distortion types that are considered to recognize the Tamil distorted characters

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