@inproceedings{baa898c130224c908c1c5bd046915286,
title = "A study of Bayesian clustering of a document set based on GA",
abstract = "In this paper, we propose new approximate clustering algorithm that improves the precision of a top-down clustering. Top-down clustering is proposed to improve the clustering speed by Iwayama et al, where the cluster tree is generated by sampling some documents, making a cluster from these, assigning other documents to the nearest node and if the number of assigned documents is large, continuing sampling and clustering from top to down. To improve precision of the top-down clustering method, we propose selecting documents by applying a GA to decide a quasi-optimum layer and using a MDL criteria for evaluating the layer structure of a cluster tree.",
keywords = "Beysian clustering, Document retrieval, Genetic algorithm, Minimum description length criteria",
author = "Keiko Aoki and Kazunori Matsumoto and Keiichiro Hoashi and Kazuo Hashimoto",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.; 2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998 ; Conference date: 24-11-1998 Through 27-11-1998",
year = "1999",
doi = "10.1007/3-540-48873-1_34",
language = "English",
isbn = "3540659072",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "260--267",
editor = "Bob McKay and Xin Yao and Newton, {Charles S.} and Jong-Hwan Kim and Takeshi Furuhashi",
booktitle = "Simulated Evolution and Learning - 2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998, Selected Papers",
}