Possibilistic Similarity Estimation and Visualization
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory(2009)
摘要
In this paper, we present a very general and powerful approach to represent and to visualize the similarity between the objects
that contain heterogeneous, imperfect and missing attributes in order to easily achieve efficient analysis and retrieval of
information by organizing and gathering these objects into meaningful groups. Our method is essentially based on possibility
theory to estimate the similarity and on the spatial, the graphical, and the clustering-based representational models to visualize
and represent its structure. Our approach will be applied to a real digestive image database (http://i3se009d.univ-brest.fr/
password view2006 [4]). Without any a priori medical knowledge concerning the key attributes of the pathologies, and without
any complicated preprocessing of the imperfect data, results show that we are capable to visualize and to organize the different
categories of the digestive pathologies. These results were validated by the doctor.
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关键词
possibilistic similarity estimation,imperfect data,clustering-based representational model,key attribute,complicated preprocessing,powerful approach,meaningful group,digestive pathology,real digestive image database,efficient analysis,different category,graph theory,scaling,clustering,possibility theory,similarity
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