Row 8102

Row ID: 8102 | Dataset Entry | Axioma AXP Content Repository

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Hello everyone,

I've been working on an algorithm that I think you might find interesting: the Pedro Thermo Similarity/Distance Algorithm. This algorithm aims to provide a more accurate alternative for text similarity and distance calculations. I've compared it with algorithms like Levenshtein, Damerau, Jaro, and Jaro-Winkler, and it has shown better results for many cases.

It also uses a dynamic approach using a 3d matrix (with a thermometer in the 3rd dimension), the complexity remains M*N, the thermometer can be considered constant. In short, the idea is to use a thermometer to treat sequential errors or successes, giving more flexibility compared to other methods that do not take this into account.

If it's not too much to ask, if you could give the repo a like, to help gain visibility, I would be very grateful. 🙏

The algorithm could be particularly useful for tasks such as data cleaning and text analysis. If you're interested, I'd appreciate any feedback or suggestions you might have.

You can find the repository here: https://github.com/pedrohcdo/PedroThermoDistance

And a detailed explanation here: https://medium.com/p/bf66af38b075

Thank you!

FieldValue
text Hello everyone, I've been working on an algorithm that I think you might find interesting: the Pedro Thermo Similarity/Distance Algorithm. This algorithm aims to provide a more accurate alternative for text similarity and distance calculations. I've compared it with algorithms like Levenshtein, Damerau, Jaro, and Jaro-Winkler, and it has shown better results for many cases. It also uses a dynamic approach using a 3d matrix (with a thermometer in the 3rd dimension), the complexity remains M*N, …
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-18
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Raw Record

{
  "text": "Hello everyone,\n\nI've been working on an algorithm that I think you might find interesting: the Pedro Thermo Similarity/Distance Algorithm. This algorithm aims to provide a more accurate alternative for text similarity and distance calculations. I've compared it with algorithms like Levenshtein, Damerau, Jaro, and Jaro-Winkler, and it has shown better results for many cases.\n\nIt also uses a dynamic approach using a 3d matrix (with a thermometer in the 3rd dimension), the complexity remains M*N, the thermometer can be considered constant. In short, the idea is to use a thermometer to treat sequential errors or successes, giving more flexibility compared to other methods that do not take this into account.\n\nIf it's not too much to ask, if you could give the repo a like, to help gain visibility, I would be very grateful. 🙏\n\nThe algorithm could be particularly useful for tasks such as data cleaning and text analysis. If you're interested, I'd appreciate any feedback or suggestions you might have.\n\nYou can find the repository here: https://github.com/pedrohcdo/PedroThermoDistance\n\nAnd a detailed explanation here: https://medium.com/p/bf66af38b075\n\nThank you!",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-18",
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