Extended Performance Graphs for Cluster Retrieval

Performance evaluations in Probabilistic Information Retrieval are often presented as Precision-Recall or Precision-Scope graphs avoiding the otherwise dominating effect of the embedding irrelevant fraction. However, precision and recall values as such offer an incomplete overview of the information retrieval system under study: information about system parameters like generality (the embedding of the relevant fraction), random performance, and the effect of varying the scope is missed. In this paper two cluster performance graphs are presented. In those cases where complete ground truth is available (both cluster size and database size) the Cluster Precision-Recall (Cluster PR) graph and the Generality-Precision=Recall graph are proposed.