The group ranking problem has received more and more attention due to its widespread applications. Traditional solution either produces an ordering list of all items or many consensus sequences as output. Unfortunately, no matter which output is generated, some weaknesses exist in the group ranking problem. Accordingly, this research combines the clustering methods with the ordering concepts to address the weaknesses of previous research, and define the consensus ordered segments as a new type of output of the group ranking problem. We also proposed an algorithm to mine consensus ordered segments from users’ ranking data. Finally, extensive experiments have been carried out using the real and synthetic dataset to demonstrate the usefulness of consensus ordered segments.