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Tagged Documents Co-Clustering


Tags are short sequences of words allowing to describe textual and non-texual resources such as as music, image or book. Tags could be used by machine information retrieval systems to access quickly a document. These tags can be used to build recommender systems to suggest similar items to a user. However, the number of tags per document is limited, and often distributed according to a Zipf law. In this paper, we propose a methodology to cluster tags into conceptual groups. Data are preprocessed to remove power-law effects and enhance the context of low-frequency words. Then, a hierarchical agglomerative co-clustering algorithm is proposed to group together the most related tags into clusters. The capabilities were evaluated on a sparse synthetic dataset and a real-world tag collection associated with scientific papers. The task being unsupervised, we propose some stopping criterion for selectecting an optimal partitioning.
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Dates and versions

hal-03933683 , version 1 (10-01-2023)


  • HAL Id : hal-03933683 , version 1


Gaëlle Candel, David Naccache. Tagged Documents Co-Clustering. World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'21) - track ICAI21, Jan 2021, Las Vegas (USA), United States. ⟨hal-03933683⟩
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