Abstract: Unsupervised ranking has a big challenge that there is no standard metric to measure the ranking results. This paper tries to propose a comparison scheme for unsupervised ranking based on ranking consensus. Some metrics used in supervised ranking can be used here to measure unsupervised ranking consensus. However, these metrics are taken between ranking lists and attributes, rather than ranking lists and ranking labels. Experimental results on UCI datasets show that RPC, which is one kind of unsupervised ranking aggregation method, has the minimum Kendall Distance and Spearman Footrule Distance than the other representative unsupervised ranking methods.