Today's applications generate a large amount of data that need to be processed by learning algorithms. In practice, the majority of the data are not associated with any labels. Unsupervised learning, i.e., clustering methods, are the most commonly used algorithms for data analysis. However, running clustering algorithms on traditional cores results in high energy consumption and slow processing speed due to a large amount of data movement between memory and processing units. In this paper, we propose DUAL, a Digital-based Unsupervised learning AcceLeration, which supports a wide range of popular algorithms on conventional crossbar memory. Instead of working with the original data, DUAL maps all data points into high-dimensional space, replacing complex clustering operations with memory-friendly operations. We accordingly design a PIM-based architecture that supports all essential operations in a highly parallel and scalable way. DUAL supports a wide range of essential operations and enables in-place computations, allowing data points to remain in memory. We have evaluated DUAL on several popular clustering algorithms for a wide range of large-scale datasets. Our evaluation shows that DUAL provides a comparable quality to existing clustering algorithms while using a binary representation and a simplified distance metric. DUAL also provides 58.8× speedup and 251.2× energy efficiency improvement as compared to the state-of-the-art solution running on GPU.