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1. Preface
2. Introduction to Transformations
3. Transformation Ports
4. Transformation Caches
6. Aggregator Transformation
7. Association Transformation
9. Case Converter Transformation
10. Classifier Transformation
11. Comparison Transformation
12. Consolidation 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
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

# Hamming Distance

Use the Hamming Distance algorithm when the position of the data characters is a critical factor, for example in numeric or code fields such as telephone numbers, ZIP Codes, or product codes.
The Hamming Distance algorithm calculates a match score for two data strings by computing the number of positions in which characters differ between the data strings. For strings of different length, each additional character in the longest string is counted as a difference between the strings.

## Hamming Distance Example

Consider the following strings:
• Morlow
• M
a
rlow
es
The highlighted characters indicate the positions that the Hamming algorithm identifies as different.
To calculate the Hamming match score, the transformation divides the number of matching characters (5) by the length of the longest string (8). In this example, the strings are 62.5% similar and the match score is
0.625
.
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