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Folksonomy mining is grasping the interest of web 2.0 community since itrepresents the core data of social resource sharing systems. However, ascrutiny of the related works interested in mining folksonomies unveils thatthe time stamp dimension has not been considered. For example, the wealthynumber of works dedicated to mining tri-concepts from folksonomies did not takeinto account time dimension. In this paper, we will consider a folksonomycommonly composed of triples <users, tags, resources> and we shall consider thetime as a new dimension. We motivate our approach by highlighting the batteryof potential applications. Then, we present the foundations for miningquadri-concepts, provide a formal definition of the problem and introduce a newefficient algorithm, called QUADRICONS for its solution to allow for miningfolksonomies in time, i.e., d-folksonomies. We also introduce a new closureoperator that splits the induced search space into equivalence classes whosesmallest elements are the quadri-minimal generators. Carried out experiments onlarge-scale real-world datasets highlight good performances of our algorithm.

A scalable mining of frequent quadratic concepts in d-folksonomies