Explaining Text Clustering Results using Semantic Structures
A. Hotho, S. Staab, and G. Stumme. Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, volume 2838 of LNAI, page 217-228. Heidelberg, Springer, (2003)
Abstract
Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
In this paper, we discuss a way of integrating a large thesaurus
and the computation of lattices of resulting clusters into common text clustering
in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.
%0 Conference Paper
%1 hotho03explaining
%A Hotho, Andreas
%A Staab, Steffen
%A Stumme, Gerd
%B Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases
%C Heidelberg
%D 2003
%E Lavrac, Nada
%E Gamberger, Dragan
%E Todorovski, Hendrik BlockeelLjupco
%I Springer
%K clustering ontology
%P 217-228
%T Explaining Text Clustering Results using Semantic Structures
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf
%V 2838
%X Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
In this paper, we discuss a way of integrating a large thesaurus
and the computation of lattices of resulting clusters into common text clustering
in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.
@inproceedings{hotho03explaining,
abstract = {Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
In this paper, we discuss a way of integrating a large thesaurus
and the computation of lattices of resulting clusters into common text clustering
in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.},
added-at = {2010-04-23T10:02:56.000+0200},
address = {Heidelberg},
author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
biburl = {https://www.bibsonomy.org/bibtex/253a943b6be4b34cf4e5329d0b58e99f6/cscholz},
booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases},
comment = {alpha},
editor = {Lavra\v{c}, Nada and Gamberger, Dragan and Todorovski, Hendrik BlockeelLjupco},
interhash = {cf66183151a5d94a0941ac6d5089ae89},
intrahash = {53a943b6be4b34cf4e5329d0b58e99f6},
keywords = {clustering ontology},
pages = {217-228},
publisher = {Springer},
series = {LNAI},
timestamp = {2010-10-20T12:45:16.000+0200},
title = {Explaining Text Clustering Results using Semantic Structures},
url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf},
volume = 2838,
year = 2003
}