The nature of statistical learning theory
The nature of statistical learning theory
Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrimination power of measures of comparison
Fuzzy Sets and Systems
Perceptually Based Metrics for the Evaluation of Textural Image Retrieval Methods
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Ranking invariance based on similarity measures in document retrieval
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Towards a Conscious Choice of a Similarity Measure: A Qualitative Point of View
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Towards a conscious choice of a fuzzy similarity measure: a qualitative point of view
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Order-based equivalence degrees for similarity and distance measures
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
ITBAM'11 Proceedings of the Second international conference on Information technology in bio- and medical informatics
Improving pervasive application behavior using other users' information
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
An analysis of peer similarity for recommendations in P2P systems
Multimedia Tools and Applications
Topological comparisons of proximity measures
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Similarity measures aim at quantifying the extent to which objects resemble each other. Many techniques in data mining, data analysis or information retrieval require a similarity measure, and selecting an appropriate measure for a given problem is a difficult task. In this paper, the diverse forms similarity measures can take are examined, as well as their relationships and respective properties. Their semantic differences are highlighted and numerical tools to quantify these differences are proposed, considering several points of view and including global and local comparisons, order-based and value-based comparisons, and mathematical properties such as derivability. The paper studies similarity measures for two types of data: binary and numerical data, i.e., set data represented by the presence or absence of characteristics and data represented by real vectors.