Hierarchical Bayesian Clustering for Automatic Text Classification
Abstract
Text classification, the grouping of texts into several clusters, has been used as a means of improving both the efficiency and the effective-Dess of text retrieval/categorization In this paper we propose a hierarchical clustering algorithm that constructs a Bet of clusters having the maximum Bayesian posterior probability, the probability that the given texts are classified into clusters We call the algorithm Hierarchical Bayesian Clustering (HBC) The advantages of HBC are experimentally verified from several viewpoints (1) HBC can re-construct the original clusters more accurately than do other non probabilistic algorithms (2) When
Cite
Text
Iwayama and Tokunaga. "Hierarchical Bayesian Clustering for Automatic Text Classification." International Joint Conference on Artificial Intelligence, 1995.Markdown
[Iwayama and Tokunaga. "Hierarchical Bayesian Clustering for Automatic Text Classification." International Joint Conference on Artificial Intelligence, 1995.](https://mlanthology.org/ijcai/1995/iwayama1995ijcai-hierarchical/)BibTeX
@inproceedings{iwayama1995ijcai-hierarchical,
title = {{Hierarchical Bayesian Clustering for Automatic Text Classification}},
author = {Iwayama, Makoto and Tokunaga, Takenobu},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1995},
pages = {1322-1327},
url = {https://mlanthology.org/ijcai/1995/iwayama1995ijcai-hierarchical/}
}