Dynamic spectrum access (DSA) has emerged as an enabling technology to allow more intensive sharing of the radio spectrum. A requirement for most proposed DSA techniques is prior knowledge of the primary user's access pattern or the ability to predict primary user activities. Therefore, spectrum surveys are taking place on an even wider scale to provide data on spectrum usage and occupancy for developing new prediction models and for spectrum planning by regulators. This paper investigates the potential of mining spectrum data for correlation between human activities in a neighborhood and the resulting spectrum occupancy across different bands. We propose a systematic approach based on two clustering techniques: Gaussian mixture models (GMMs) and self-organizing map neural networks (SOMNNs). We mine spectrum measurements gathered by our network of spectrum observatories in Virginia and Illinois. The results confirm the existence of observable correlation and show that our proposed techniques detect correlation across various land mobile radio (LMR) and cellular bands under a wide range of scenarios with a high detection ratio. These results inspire us to develop more efficient prediction models for applications in opportunistic spectrum access (OSA) or self-organized networks.