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 Output Types

Match Output Types

The Match Output view includes options that specify the output data format. You can configure the transformation to write records in clusters or in matched pairs.
Select one of the following match output types:
Best Match
Writes each record in the master data set with the record that represents the best match in the second data set. The match operation selects the record in the second data set that has the highest match score for the master record. If two or more records return the highest score, the match operation selects the first record in the second data set. Best Match writes each pair of records to a single row.
You can select
Best Match
when you configure the transformation for dual-source analysis.
Clusters - Best Match
Writes clusters that represent the best match between one record and another record in the same data set or between two data sets. The match score between the two records must meet the match threshold. Best match clusters can contain more than two records if a record represents the best match with more than one other record.
You can select
Clusters - Best Match
in any type of identity analysis.
The index data storage method that you select can affect the contents of the cluster output in
Clusters - Best Match
mode. A transformation that connects to index tables can create different clusters than a transformation that stores index data for the same records in temporary files. The index data storage method does not affect the match scores that the transformation generates for pairs of records.
Clusters - Match All
Writes clusters of records that match with a score that meets the match threshold. Each record must match at least one other record in the cluster.
You can select
Clusters - Match All
in any type of identity analysis.
Matched Pairs
Writes all pairs of records that match each other with a score that meets the match threshold. The transformation writes each pair to a single row and adds the match score for each pair to each row. If a record matches more than one other record, the transformation writes a row for each record pair.
You can select
Matched Pairs
in any type of identity analysis.

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