Subject
Formal concept analysis (FCA) is increasingly applied to
data mining problems, essentially as a formal framework for mining reduced
representations (bases) of target pattern families. Yet most of the
FCA-based miners, closed pattern miners, would only extract the patterns
themselves out of a dataset, whereas the generality order among
patterns would be required for many bases. As a contribution to the
topic of the (precedence) order computation on top of the set of closed
patterns, we present a novel method that borrows its overall incremental
approach from two algorithms in the literature. The claimed innovation
consists of splitting the update of the precedence links into a large
number of lower-cover list computations (as opposed to a single uppercover
list computation) that unfold simultaneously. The resulting method
shows a good improvement with respect to its counterpart both on its
theoretical complexity and on its practical performance. It is therefore
a good starting point for the design of efficient and scalable precedence
miners.
%0 Journal Article
%1 JaumeBaixeries&LaszloSzathmary&PetkoValtchev_2009
%A Baixeries, Jaume
%A Szathmary, Laszlo
%A Valtchev, Petko
%A Godin, Robert
%D 2009
%K imported
%T Yet a Faster Algorithm for Building the Hasse Diagram of a Concept Lattice
%X Subject
Formal concept analysis (FCA) is increasingly applied to
data mining problems, essentially as a formal framework for mining reduced
representations (bases) of target pattern families. Yet most of the
FCA-based miners, closed pattern miners, would only extract the patterns
themselves out of a dataset, whereas the generality order among
patterns would be required for many bases. As a contribution to the
topic of the (precedence) order computation on top of the set of closed
patterns, we present a novel method that borrows its overall incremental
approach from two algorithms in the literature. The claimed innovation
consists of splitting the update of the precedence links into a large
number of lower-cover list computations (as opposed to a single uppercover
list computation) that unfold simultaneously. The resulting method
shows a good improvement with respect to its counterpart both on its
theoretical complexity and on its practical performance. It is therefore
a good starting point for the design of efficient and scalable precedence
miners.
@article{JaumeBaixeries&LaszloSzathmary&PetkoValtchev_2009,
abstract = {Subject
Formal concept analysis (FCA) is increasingly applied to
data mining problems, essentially as a formal framework for mining reduced
representations (bases) of target pattern families. Yet most of the
FCA-based miners, closed pattern miners, would only extract the patterns
themselves out of a dataset, whereas the generality order among
patterns would be required for many bases. As a contribution to the
topic of the (precedence) order computation on top of the set of closed
patterns, we present a novel method that borrows its overall incremental
approach from two algorithms in the literature. The claimed innovation
consists of splitting the update of the precedence links into a large
number of lower-cover list computations (as opposed to a single uppercover
list computation) that unfold simultaneously. The resulting method
shows a good improvement with respect to its counterpart both on its
theoretical complexity and on its practical performance. It is therefore
a good starting point for the design of efficient and scalable precedence
miners.},
added-at = {2013-08-04T13:35:40.000+0200},
author = {Baixeries, Jaume and Szathmary, Laszlo and Valtchev, Petko and Godin, Robert},
biburl = {https://www.bibsonomy.org/bibtex/29a4905a637a6a777cf5ea9686f0a0243/francesco.k},
interhash = {bd64cf945df697888d793d02626592fe},
intrahash = {9a4905a637a6a777cf5ea9686f0a0243},
keywords = {imported},
timestamp = {2013-08-04T14:07:27.000+0200},
title = {Yet a Faster Algorithm for Building the Hasse Diagram of a Concept Lattice},
urldate = {15.07.2012},
year = 2009
}