Focusing on the precise definition of information measures for interval-valued fuzzy soft sets, the distance measure, the similarity measure, the entropy measure, the inclusion measure, and the subsethood measure of interval-valued fuzzy soft sets were introduced. A series of formulae of information measures were presented, and their transformation relationships were discussed. Then, combining the characteristics of interval-valued fuzzy soft sets, a clustering algorithm based on similarity measure was explored. It emphasized the clustering of similar level knowledge of experts who gave the evaluation of objects. Meanwhile, the computational complexity of the algorithm was discussed. Finally, a practical example was given to prove that the proposed algorithm can effectively handle the clustering problem of experts.