Jaccard Similarity Algorithm | Machine Learning

[Arko-Sengupta]

Jaccard Similarity Algorithm

Overview

Introduces a New Implementation of the Jaccard Similarity Algorithm in the JaccardSimilarity class. The Jaccard Similarity is a classical metric used in Natural Language Processing and Information Retrieval to measure the similarity between two sets based on their intersection and union.

Key Features

• Set Representation: Converts input text into sets of words for comparison.
• Tokenization: Splits the input strings into words based on whitespace.
• Intersection Calculation: Determines the common elements between two sets of words.
• Union Calculation: Computes the total unique elements in both sets combined.
• Jaccard Similarity Calculation: Measures the ratio of the intersection size to the union size, providing a similarity score ranging from 0 to 100%.
• Percentage Output: Provides the similarity score as a percentage for easier interpretation.

Mathematical Foundation

• Intersection: The number of elements common to both sets.

• Union: The total number of unique elements in both sets combined.

• Jaccard Similarity Formula:

                              Jaccard Similarity = (Size of Intersection) / (Size of Union)
  

where the result is expressed as a percentage, with 100% indicating identical sets and 0% indicating no overlap.

Usage

• The JaccardSimilarity class provides a method to calculate the similarity between two strings. It includes:
• jaccard_similarity(str1, str2): Computes the Jaccard similarity between two input strings as a percentage.

Error Handling

Robust Error Handling is implemented to ensure reliable calculations. Any issues, such as empty input strings, are raised with appropriate error messages and logged.

Benefits

• Provides a straightforward method for comparing text based on set operations.
• Useful for applications including document similarity, plagiarism detection, and information retrieval.
• Easy to understand and implement with minimal dependencies.

[algorithms-keeper]

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