The theory of concept (or Galois) lattices provides a natural and
formal setting in which to discover and represent concept hierarchies.
In this paper we present a system, GALOIS, which is able to determine
the concept lattice corresponding to a given set of objects. GALOIS is
incremental and relatively efficient, the time complexity of each
update ranging from O(n) to O(n2) where n is the number of concepts in
the lattice. Unlike most approaches to conceptual clustering, GALOIS
represents and updates all possible classes in a restricted concept
space. Therefore the concept hierarchies it finds are always justified
and are not sensitive to object ordering. We experimentally
demonstrate, using several machine learning data sets, that GALOIS can
be successfully used for class discovery and class prediction. We also
point out applications of GALOIS in fields related to machine learning
(i.e., information retrieval and databases).
%0 Conference Paper
%1 carpineto1993galois
%A Carpineto, Claudio
%A Romano, Giovanni
%B ICML
%D 1993
%K imported
%P 33--40
%T GALOIS: An Order-Theoretic Approach to Conceptual Clustering.
%U http://www.researchgate.net/publication/221345369_GALOIS_An_Order-Theoretic_Approach_to_Conceptual_Clustering/file/79e41508a54d85e1a1.pdf
%V 90
%X The theory of concept (or Galois) lattices provides a natural and
formal setting in which to discover and represent concept hierarchies.
In this paper we present a system, GALOIS, which is able to determine
the concept lattice corresponding to a given set of objects. GALOIS is
incremental and relatively efficient, the time complexity of each
update ranging from O(n) to O(n2) where n is the number of concepts in
the lattice. Unlike most approaches to conceptual clustering, GALOIS
represents and updates all possible classes in a restricted concept
space. Therefore the concept hierarchies it finds are always justified
and are not sensitive to object ordering. We experimentally
demonstrate, using several machine learning data sets, that GALOIS can
be successfully used for class discovery and class prediction. We also
point out applications of GALOIS in fields related to machine learning
(i.e., information retrieval and databases).
@inproceedings{carpineto1993galois,
abstract = {The theory of concept (or Galois) lattices provides a natural and
formal setting in which to discover and represent concept hierarchies.
In this paper we present a system, GALOIS, which is able to determine
the concept lattice corresponding to a given set of objects. GALOIS is
incremental and relatively efficient, the time complexity of each
update ranging from O(n) to O(n2) where n is the number of concepts in
the lattice. Unlike most approaches to conceptual clustering, GALOIS
represents and updates all possible classes in a restricted concept
space. Therefore the concept hierarchies it finds are always justified
and are not sensitive to object ordering. We experimentally
demonstrate, using several machine learning data sets, that GALOIS can
be successfully used for class discovery and class prediction. We also
point out applications of GALOIS in fields related to machine learning
(i.e., information retrieval and databases).},
added-at = {2013-08-04T16:45:40.000+0200},
author = {Carpineto, Claudio and Romano, Giovanni},
biburl = {https://www.bibsonomy.org/bibtex/2965179607247f90640cc2a9c50f4c770/francesco.k},
booktitle = {ICML},
citations = {206},
citedbyid = {4126371096205247359},
file = {file://GALOIS An Order-Theoretic Approach to conceptual clustering.pdf:pdf},
interhash = {a46d76ec64ba80443be0565119e9469e},
intrahash = {965179607247f90640cc2a9c50f4c770},
keywords = {imported},
mailhosts = {itcaspur.bit},
md5sum = {35f4b0fc5357ddb78479903dac92f059},
pages = {33--40},
pdfmeat = {timestamp: 2013-08-04 16:44:17; queries: 1; inode: 1703986},
timestamp = {2013-08-04T16:45:40.000+0200},
title = {GALOIS: An Order-Theoretic Approach to Conceptual Clustering.},
url = {http://www.researchgate.net/publication/221345369_GALOIS_An_Order-Theoretic_Approach_to_Conceptual_Clustering/file/79e41508a54d85e1a1.pdf},
volume = 90,
year = 1993
}