Focusing on the issue that feature selection for the usually encountered large scale data sets in the "big data" is too slow to meet the practical requirements, a fast feature selection algorithm for unsupervised massive data sets was proposed based on the incremental absolute reduction algorithm in traditional rough set theory. Firstly, the large scale data set was regarded as a random object sequence and the candidate reduct was set empty. Secondly, random object was one by one drawn from the large scale data set without replacement; next, each random drawn object was checked if it could be distinguished with the other objects in the current object set and then merged with current object set, if the new object could not be distinguished using the candidate reduct, a new attribute that can distinguish the new object should be added into the candidate reduct. Finally, if successive I objects were distinguishable using the candidate reduct, the candidate reduct was used as the reduct of the large scale data set. Experiments on five unsupervised large-scale data sets demonstrated that a reduct which can distinguish no less than 95% object pairs could be found within 1% time needed by the discernibility matrix based algorithm and incremental absolute reduction algorithm. In the experiment of the text topic mining, the topic found by the reducted data set was consistent with that of the original data set. The experimental results show that the proposed algorithm can obtain effective reducts for large scale data set in practical time.