Iterative Processing: The K-means algorithm is iterative. Each iteration involves calculating distances between data points and centroids, reassigning points, and recalculating centroids. For very large datasets, these iterations malaysia phone number list can be computationally intensive and involve multiple passes over the data.
In-Database Analytics: Modern data warehouses (like Snowflake, Google BigQuery, Amazon Redshift) and analytical databases often offer in-database machine learning capabilities, including built-in functions for K-means clustering. This allows the computation to happen where the data resides, minimizing data movement.
Distributed Processing: For extremely large datasets, K-means can be implemented using distributed computing frameworks (like Apache Spark) that interact with distributed databases or data lakes.