1 research outputs found
A scalable mining of frequent quadratic concepts in d-folksonomies
Folksonomy mining is grasping the interest of web 2.0 community since it
represents the core data of social resource sharing systems. However, a
scrutiny of the related works interested in mining folksonomies unveils that
the time stamp dimension has not been considered. For example, the wealthy
number of works dedicated to mining tri-concepts from folksonomies did not take
into account time dimension. In this paper, we will consider a folksonomy
commonly composed of triples and we shall consider the
time as a new dimension. We motivate our approach by highlighting the battery
of potential applications. Then, we present the foundations for mining
quadri-concepts, provide a formal definition of the problem and introduce a new
efficient algorithm, called QUADRICONS for its solution to allow for mining
folksonomies in time, i.e., d-folksonomies. We also introduce a new closure
operator that splits the induced search space into equivalence classes whose
smallest elements are the quadri-minimal generators. Carried out experiments on
large-scale real-world datasets highlight good performances of our algorithm