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  • Common Content for Data Engineering 10.5.9
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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. Macro Transformation
  30. Match Transformation
  31. Match Transformations in Field Analysis
  32. Match Transformations in Identity Analysis
  33. Normalizer Transformation
  34. Merge Transformation
  35. Parser Transformation
  36. Python Transformation
  37. Rank Transformation
  38. Read Transformation
  39. Relational to Hierarchical Transformation
  40. REST Web Service Consumer Transformation
  41. Router Transformation
  42. Sequence Generator Transformation
  43. Sorter Transformation
  44. SQL Transformation
  45. Standardizer Transformation
  46. Union Transformation
  47. Update Strategy Transformation
  48. Web Service Consumer Transformation
  49. Parsing Web Service SOAP Messages
  50. Generating Web Service SOAP Messages
  51. Weighted Average Transformation
  52. Window Transformation
  53. Write Transformation
  54. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

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Viewing Match Cluster Analysis Data

Viewing Match Cluster Analysis Data

You can view statistical data on the clusters that the transformation can create. The cluster statistics summarize the level of record duplication in the data set based on the current mapping configuration.
To view the data, right-click the Match transformation in the mapping canvas and select
Match Cluster Analysis
.
Before you run the analysis, validate the mapping that contains the transformation.
Match cluster analysis displays data for the following properties:
Property
Description
Source
The number of input data rows.
Last run
The date and time of the analysis.
Total number of discovered clusters
The number of clusters that the match analysis generates when the mapping runs.
Minimum cluster size
The number of records in the cluster or clusters that contain the fewest records. If the minimum cluster size is 1, the data set contains at least one unique record.
Maximum cluster size
The number of records in the cluster or clusters that contain the most records.
If this value greatly exceeds the average cluster size, the largest cluster might contain false duplicates.
Number of unique records
The number of records in the data set that do not match another record with a score that meets the match threshold.
Number of duplicate records
The number of records in the data set that match another record with a score that meets the match threshold.
Total comparisons
The number of comparison operations that the mapping performs.
Average cluster size
The average number of records in a cluster.
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