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Query-oriented clustering: a multi-objective approach.

Tassadit Amghar
  • Function : Author
Bernard Levrat
  • Function : Author
Frédéric Saubion


Document clustering techniques have been widely applied in Information Retrieval to reorganize results furnished as a response to user's queries. Following the Cluster Hypothesis which states that relevant documents tend to be more similar one to each other than to non-relevant ones, most of relevant documents are likely to be gathered in a single cluster. Usually, systems organizing search results as a set of clusters consider this tendency as a very advantageous phenomenon, since it allows to filter the results provided by the initial search. Adopting a different point of view, we rather consider the Cluster Hypothesis as a hindrance to the information access since it prevents the emergence of the various aspects of the query. The risk induced is to restrict the perception of the subject to an unique point of view. Therefore, we propose to rather distribute the relevant documents over clusters by orienting the organization of the clusters according to the user's topic. The aim is to attract the clusters around the latter in order to highlight the thematic differences between documents which are strongly connected to the query. Rather than modifying the inter-documents similarity computation as it is the case in several studies, we propose to directly act on the organization of the clusters by using a multi-objective evolutionary clustering algorithm which, besides the classical internal cohesion, also optimizes the query proximity of the clusters. First experimental results highlight the great benefit which may be gained by our way of query consideration.
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hal-01291872 , version 1 (22-03-2016)



Sylvain Lamprier, Tassadit Amghar, Bernard Levrat, Frédéric Saubion. Query-oriented clustering: a multi-objective approach.. ACM Symposium on Applied Computing, Mar 2010, Sierre, Switzerland. pp.1789-1795, ⟨10.1145/1774088.1774467⟩. ⟨hal-01291872⟩
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