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A Study on Similarity Join in Heterogeneous Information Network

A Study on Similarity Join in Heterogeneous Information Networkvon Sandhya S. Sie sparen 18% des UVP sparen 18%
Über A Study on Similarity Join in Heterogeneous Information Network

A similarity join is a fundamental operation in data mining and database management, used to identify pairs of records from different datasets that are similar to each other based on some defined similarity metric. The primary goal of a similarity join is to find pairs of records that have similar attributes or features, even if the records are not identical. This is particularly useful when dealing with data from various sources that may contain variations, errors, or inconsistencies. Path-based algorithms for similarity join refer to techniques that utilize paths (sequences of edges) connecting nodes in a graph to determine the similarity between pairs of nodes. These algorithms are commonly used in various fields, such as graph mining, network analysis, and data integration.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9786206768548
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 92
  • Veröffentlicht:
  • 12. September 2023
  • Abmessungen:
  • 150x6x220 mm.
  • Gewicht:
  • 155 g.
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Beschreibung von A Study on Similarity Join in Heterogeneous Information Network

A similarity join is a fundamental operation in data mining and database management, used to identify pairs of records from different datasets that are similar to each other based on some defined similarity metric. The primary goal of a similarity join is to find pairs of records that have similar attributes or features, even if the records are not identical. This is particularly useful when dealing with data from various sources that may contain variations, errors, or inconsistencies. Path-based algorithms for similarity join refer to techniques that utilize paths (sequences of edges) connecting nodes in a graph to determine the similarity between pairs of nodes. These algorithms are commonly used in various fields, such as graph mining, network analysis, and data integration.

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