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Author Canessa, E.; Chaigneau, S.E.; Moreno, S.; Lagos, R. doi  openurl
  Title Informational content of cosine and other similarities calculated from high-dimensional Conceptual Property Norm data Type
  Year 2020 Publication Cognitive Processing Abbreviated Journal Cogn. Process.  
  Volume 21 Issue Pages 601-614  
  Keywords Cosine similarity; Euclidean distance; Chebyshev distance; Clustering; Conceptual properties  
  Abstract To study concepts that are coded in language, researchers often collect lists of conceptual properties produced by human subjects. From these data, different measures can be computed. In particular, inter-concept similarity is an important variable used in experimental studies. Among possible similarity measures, the cosine of conceptual property frequency vectors seems to be a de facto standard. However, there is a lack of comparative studies that test the merit of different similarity measures when computed from property frequency data. The current work compares four different similarity measures (cosine, correlation, Euclidean and Chebyshev) and five different types of data structures. To that end, we compared the informational content (i.e., entropy) delivered by each of those 4 x 5 = 20 combinations, and used a clustering procedure as a concrete example of how informational content affects statistical analyses. Our results lead us to conclude that similarity measures computed from lower-dimensional data fare better than those calculated from higher-dimensional data, and suggest that researchers should be more aware of data sparseness and dimensionality, and their consequences for statistical analyses.  
  Address [Canessa, Enrique; Chaigneau, Sergio E.] Univ Adolfo Ibanez, Sch Psychol, Ctr Cognit Res CINCO, Ave Presidente Errazuriz 3328, Santiago, Chile, Email:  
  Corporate Author Thesis (up)  
  Publisher Springer Heidelberg Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1612-4782 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000546845700001 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1180  
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