Semi-supervised k-means clustering for outlier detection in mammogram classification
K. Thangavel, Abubacker Kaja Mohideen · 2010
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 classes. The clusters are compared with original classes, the wrongly clustered instances are identified as outliers and they are removed from the feature space. A novel Genetic Association Rule Miner (GARM) is applied with this reduced feature set to construct the association rules for classification. The performance is analyzed with rough set using Receiver Operating Characteristic (ROC) curve analysis. The mammogram images from MIAS (Mammogram Image Analysis Society) and DDSM (Digital Database for Screening Mammography) were used to evaluate the performance.