"K" in Machine Learning: K-Nearest Neighbors (KNN) and K-means Clustering
While not databases themselves, K-Nearest Neighbors and K-means clustering are fundamental machine learning algorithms that involve the concept of "K" and heavily rely on efficient data storage and retrieval, often interacting with database systems.
What it is: KNN is a non-parametric, lazy learning algorithm used for classification and regression. The "K" refers to the number of nearest data points (neighbors) used to make a prediction.
Database Interaction:
Similarity Search: The core of KNN involves finding the "K" data points in a dataset that are most similar to a given query point (e.g., using Euclidean distance). For large datasets, this requires efficient similarity search capabilities within a database.
Vector Databases / Nearest Neighbor Search (NNS) Indexes: Traditional japan phone number list relational databases are poor at efficient similarity searches on high-dimensional data (known as the "curse of dimensionality"). This has led to the rise of:
Vector Databases: Specialized databases designed to store and query high-dimensional vectors (embeddings) efficiently. Examples include Pinecone, Weaviate, Milvus, Qdrant. These databases use specialized indexing techniques like Annoy (Approximate Nearest Neighbors Oh Yeah), FAISS (Facebook AI Similarity Search), or HNSW (Hierarchical Navigable Small World) to quickly find approximate nearest neighbors.
Database Extensions: Some traditional databases (e.g., PostgreSQL with pg_embedding or pgvector extensions) have added capabilities for storing and querying vector embeddings, making them more suitable for KNN-like operations.