In this paper we discuss a new approach for the detection of clustered microcalcifications (MCs) in mammograms. MCs are an important early sign of breast cancer in women. Their accurate detection is a key issue in computer aided detection scheme. To improve the performance of detection, we propose a Bagging and Boosting based twin support vector machine (BB-TWSVM) to detect MCs. The algorithm is composed of three modules: the image pro-processing, the feature extraction component and the BB-TWSVM module. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed by using a simple artifact removal filter and a well designed high-pass filter. Then the combined image feature extractors are employed to extract 164 image features. In the combined image features space, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained BB-TWSVM is used as a classifier to make decision for the presence of MCs or not. The experimental results of this study indicate the potential of the approach for computer-aided detection of breast cancer.