Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Match Score Calculations

Match Score Calculations

The match score is a numerical value that represents the degree of similarity between two column values. An algorithm calculates a match score as a decimal value in the range 0 through 1. An algorithm assigns a score of 1 when two column values are identical.
When you select multiple column pairs for analysis, the transformation calculates an average score based on the scores in the selected columns. By default, the transformation assigns equal weight to scores from each pair of columns. The transformation does not infer the relative importance of the column data in the data set.
You can edit the weight values that the transformation uses to calculate the match score. Edit the weight values when you want to assign higher or lower priority to columns in the data set.
You can also set the scores that the transformation applies when it finds a null value in a column. By default, the transformation treats null values as data errors and assigns a low match score to any pair of values that contains a null.
The algorithm you select determines the match score between two values. The algorithm generates a single score for the two values. The match scores do not depend on the type of match output or the type of scoring method you select.

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