In the scenarios of diagnosing bearing faults for large rotary machinery, the ower identification accuracy existed in traditional fault identification technique. Hence, based on the frequency-domain analysis, a novel data mining framework called associated frequency patterns mining framework (AFPMF) was proposed in this paper, which was consisted of data pre-processing, associated frequency pattern mining process and fault status monitoring. In the data pre-processing of AFPMF, time window was adopted to divide the machinery vibration data stream into multiple sub-blocks, and then Fast Fourier Transform (FFT) was employed to make the data sub-blocks time-frequency transform for frequency feature extraction. The associated frequency pattern tree with sliding window was also used to build a compact tree for data mining. Finally, the potential fault status with the vibration frequency existed in the mining results was identified to realize the fault status monitoring. After the comparison of AFPMF and the traditional methods in the bearing fault diagnosis, the results show that AFPMF had higher identification accuracy than other traditional ones.