Breast cancer is a leading cause of cancer type for death among women in most of popular countries, breast cancer detection is important and challenging role in worldwide to save women's life. Due to inexperience to detect cancer, the doctors and radio logistic can miss the abnormality, which leads to death. Mammography is the most used method for breast cancer detection used by the radiologists. In this experiment, the MIAS (Mammogram Image Analysis Society) database is used and the MIAS database consists of normal and abnormal type of 322 mammograms. The pre-processing is most important step to capture quality mammogram image for next study and processing in mammogram analysis. Texture analysis plays important role to identify normal and abnormal types. Texture feature extraction can be done by local binary patterns (LBP) operator and by using LBP we can consider only sign parameters, it may loss the some texture information. The local binary pattern is a rotation invariant approach for the texture analysis. In this experiment famous completed LBP (CLBP) method used for extracting texture features. Completed LBP considering the sign, magnitude and centre gray level values. By using the joint or hybrid distributions combine CLBP_Sign, CLBP_Magnitude and CLBP_Center gray level values.LBP is one type of Completed LBP for texture analysis, advantage of CLBP is rotation invariant. Finally extracted texture features can be trained and classified by using the SVM classifier for identifying the normal and abnormal cancer type.
Abstract. Association Rule Mining (ARM) with reference to fuzzy logic is used to further data mining tasks for classification and clustering. Traditional Fuzzy ARM algorithms have failed to mine rules from high-dimensional data efficiently, since those are meant to deal with relatively much less number of attributes or dimensions. Fuzzy ARM with high-dimensional data is a challenging problem to be addressed. This paper uses a quick and economical Fuzzy ARM algorithm FAR-HD, which processes frequent item sets using a two-phased multiple-partition approach especially for large high-dimensional datasets. The proposed algorithm is an extension to the FAR-HD process in which it improves the accuracy in terms of associative soft category labels by building a framework for fuzzy associative classifier to leverage the functionality of fuzzy association rules. Fuzzy ARM represent latent and dominant patterns in the given dataset, such a classifier is anticipated to supply superb accuracy particularly in terms of fuzzy support.
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