@inproceedings{1699efcd004743129ad584c5c00bab10,
title = "An investigation on the compression quality of aiNet",
abstract = "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 reasonable results on an uniformly distributed data set, but poor results on non-uniformly distributed data sets, i.e. data sets which contain dense point regions. This effect is caused by the optimization criterion of aiNet.",
keywords = "clustering algorithms, immune system, computer science, computational intelligence, density measurement, continuous production, mirrors, helium, data compression, pattern recognition",
author = "Thomas Stibor and Jonathan Timmis",
year = "2007",
doi = "10.1109/FOCI.2007.371518",
language = "English",
isbn = "1424407036",
series = "Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "495--502",
booktitle = "Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007",
address = "United States of America",
note = "2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007 ; Conference date: 01-04-2007 Through 05-04-2007",
}