Understanding Cosine Similarity: A Key Concept in Data Science
In the realm of data science, similarity measures are crucial for various applications, such as information retrieval, natural language processing, and clustering. One of the most widely used similarity measures is cosine similarity. Despite its mathematical nature, cosine similarity is a concept that can be easily understood and applied to many real-world problems. In this article, we’ll break down cosine similarity, explore its mathematical foundation, and highlight its practical applications.
What is Cosine Similarity?
Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space. It is defined as the cosine of the angle between the two vectors. In simpler terms, cosine similarity measures the cosine of the angle between two vectors projected in a multi-dimensional space.
Mathematical Definition
The cosine similarity between two vectors A and B can be calculated using the following formula:
