As cyber attacks grow in complexity and frequency, cyber threat intelligence (CTI) remains a priority objective for defenders. A critical component of CTI at the strategic level of defensive operations is attack attribution. Attributing an attack to a threat group informs defenders on adversaries that are actively engaging them and advances their ability respond. In this paper, we propose a data analytic approach towards threat attribution using adversary playbooks of tactics, techniques, and procedures (TTPs). Specifically, our approach uses association rule mining on a large real world CTI dataset to extend known threat TTP playbooks with statistically probable TTPs the adversary may deploy. The benefits are twofold. First, we offer a dataset of learned TTP associations and extended threat playbooks. Second, we show that we can attribute attacks using a weighted Jaccard similarity with 96\% accuracy.