Similarity (Data Science) 편집하기 (부분)

==Applications of Similarity in Data Science==
Similarity metrics play a critical role in various data science tasks:
*'''Clustering''': Similarity metrics are used to group data points into clusters based on their resemblance, such as in k-means or hierarchical clustering.
*'''Recommendation Systems''': Recommender algorithms, such as collaborative filtering, rely on similarity to suggest items that are similar to what a user has liked or viewed.
*'''Text Analysis''': Similarity measures like cosine similarity are used in natural language processing to compare document or sentence vectors.
*'''Anomaly Detection''': Outliers are often detected by examining data points that are significantly different from others, based on similarity or distance measures.
*'''Image Recognition''': In computer vision, similarity metrics are used to compare features of images, aiding in tasks like image matching and retrieval.