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Euclidean distance is mainly used to calculate likeness between two functions. Dependency graph generation and Dependency Graph Matching are two phases of the dependency measurement. To generate the dependency graph primarily the Entropy, Joint Entropy, Conditional Entropy, Relative Entropy and Mutual information rooted in information theory. Between the two dependencies graph of schema matching process is done b the distance measure, Euclidean distance is used for matching schema. This paper presents the connection between of entropy and Mutual information in Euclidean distance.
"Entropy and Mutual Information in Relation to Euclidean Distance for Dependency Graph Generations", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a417-a425, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601057.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator