In order to improve the performance of these algo-
rithms, the representation of contexts through struc-
tures such as BDD has showed to be an interesting
and efﬁcient alternative in information retrieval.
The paper presented a number of challenges, such
as generating triadic contexts through a synthetic gen-
erator of dyadic contexts. In addition, contexts must
respect the formal triadic deﬁnition that all conditions
have the same amount of attributes.
Through this study, it is noticed that the problem
of high dimensionality in triadic contexts already hap-
pens with a reduced number of objects, attributes (and
conditions) when compared to the dyadic approach.
The tests performed showed that the TRIAS algo-
rithm, for example, can not handle dimensions char-
acterized as dyadic high dimensionality and showed
to be inefﬁcient when used with larger context for the
triadic approach.
The retrieval of objects, attributes and conditions
showed to be efﬁcient since operations are performed
under contexts and variables represented as BDDs.
Unlike conventional structures such as lists, queues,
and stacks, where computational complexity is re-
quired, BDDs provide attribute extraction through
unique logical operations that reduce the retrieval
time of elements in a context.
As a future work, we intend to implement a BDD
version of the TRIAS algorithm. The objective is to
reduce the time of the queries performed in order to
classify the subset of newly discovered concepts and
consequently increase the extractive power of more
frequent triadic concepts in a triadic context. It is
also expected to reduce execution time since the re-
sults presented in Table 4 proved to be infeasible.
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