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