Selecting an appropriate software clustering algorithm that can help the process of understanding a large software system is a challenging issue. The effectiveness of a particular algorithm may be influenced by a number of different factors, such as the types of decompositions produced, or the way clusters are named. In this paper, we introduce an effectiveness measure for software clustering algorithms based on MoJo distance, and describe an algorithm that calculates its value. We also present experiments that demonstrate its improved performance over previous measures, and show how it can be used to assess the effectiveness of software clustering algorithms.