A new evaluation measure for information retrieval systems

Abstract— Some of the established approaches to evaluating text clustering algorithms for information retrieval show theoretical flaws. In this paper, we analyze these flaws and introduce a new evaluation measure to overcome them. Based on a simple yet rigorous mathematical analysis of the effect of certain parameters in cluster based retrieval, we show that certain conclusions drawn in the recent literature must be taken with a grain of salt. Our new measure, in contrast, accounts for statistical biases that have to be expected according to our analysis. A series of experiments and a comparison with results reported recently underlines that this measure is a more suitable performance indicator that allows for more meaningful interpretations.

• Type: Conference
• Year: 2007
• Where: SMC
• Authors: Martin Mehlitz, Christian Bauckhage, Jérôme Kunegis, Sahin Albayrak
• Keywords: Cluster Based Retrieval, Control Systems, Rigorous Mathematical Analysis, SMC 2007, Text Clustering Algorithms