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AiNet is an immune-inspired algorithm for data compression, i.e. the reduction of redundancy in data sets. In this paper we investigate the compression quality of aiNet. Therefore, a similarity measure between input set and reduced output set is presented which is based on the Parzen window estimation and the Kullback-Leibler divergence. Four different artificially generated data sets are created and the compression quality is investigated. Experiments reveal that aiNet produced reasonabledoi:10.1109/foci.2007.371518 dblp:conf/foci/StiborT07 fatcat:oyqw5liig5gfdislyubr2saqbu