Yogi Satrya Aryadinata and Anne Laurent and Michel Sala
M2LGP Mining Multiple Level Gradual Patterns
353 - 360
2013
7
3
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/2869
https://publications.waset.org/vol/75
World Academy of Science, Engineering and Technology
Gradual patterns have been studied for many years as
they contain precious information. They have been integrated in
many expert systems and rulebased systems, for instance to reason
on knowledge such as “the greater the number of turns, the greater
the number of car crashes”. In many cases, this knowledge has been
considered as a rule “the greater the number of turns → the greater
the number of car crashes” Historically, works have thus been
focused on the representation of such rules, studying how implication
could be defined, especially fuzzy implication. These rules were
defined by experts who were in charge to describe the systems they
were working on in order to turn them to operate automatically. More
recently, approaches have been proposed in order to mine databases
for automatically discovering such knowledge. Several approaches
have been studied, the main scientific topics being how to determine
what is an relevant gradual pattern, and how to discover them as
efficiently as possible (in terms of both memory and CPU usage).
However, in some cases, endusers are not interested in raw level
knowledge, and are rather interested in trends. Moreover, it may be
the case that no relevant pattern can be discovered at a low level of
granularity (e.g. city), whereas some can be discovered at a higher
level (e.g. county). In this paper, we thus extend gradual pattern
approaches in order to consider multiple level gradual patterns. For
this purpose, we consider two aggregation policies, namely
horizontal and vertical.
Open Science Index 75, 2013