In this era of digitalization, data has widely been used in control engineering. While stability analysis is a mainstay for control science, most stability analysis tools still require explicit knowledge of the model or a high-fidelity simulator. In this work, a new data-driven Lyapunov analysis framework is proposed. Without using the model or its simulator, the proposed approach can learn a piece-wise affine Lyapunov function with a finite and fixed off-line dataset. The learnt Lyapunov function is robust to any dynamics that are consistent with the off-line dataset. Along the development of proposed scheme, the Lyapunov stability criterion is generalized. This generalization enables an iterative algorithm to augment the region of attraction.