Coronal mass ejection (CME) is a highly energetic solar phenomenon. It has a significant impact on the space weather in the near-Earth environment. With the accumulation of CME observations, it becomes more challenging to handle them manually. Therefore, we need an automatic method for identifying CMEs. We propose an unsupervised method for classifying and detecting changes in CMEs. The method consists of four main steps: (i) feature extraction: features derived from difference-image and features derived from pretrained convolutional neural networks (CNN), (ii) dimensional reduction using Principal Component Analysis (PCA), (iii) unsupervised classification using K-mean clustering based on PCA components and (iv) morphological post-processing to improve the clustering output. We compare the results with manual catalog (e.g., coordinated data analysis workshops (CDWA) data center) and automatic detection catalogs (e.g., solar eruption detection system (SEEDS), computer-aided CME tracking (CACTus) and coronal image processing (CORIMP)). The comparison is based on CME characteristics (e.g., time of first appearance, position angle, angular width and velocity). We demonstrate the benefit of this unsupervised method, which produces comparable results to classical methods.