Nathan Smith, Senior Data Scientist at Neo4j
Feb 29
13 mins read
In a previous article, I discussed the benefits of using k-medoids to cluster graph data. In the k-medoids approach, you determine how many clusters you would like to partition the graph into. This number is called k. The algorithm identifies a set of k nodes in the graph called medoids. The other... read more
Nathan Smith, Senior Data Scientist at Neo4j
Nov 08, 2023
12 mins read
K-medoids is an approach for discovering clusters in data. It is similar to the well-known k-means algorithm.Both approaches require the analyst to select the number of output clusters before running the algorithm. This number is called k. Both algorithms assign each dataset member to one of... read more
Nathan Smith, Senior Data Scientist at Neo4j
Oct 01, 2021
6 mins read
Cluster related products and separate conflicting entities with the newest algorithm in the Graph Data Science Library: Approximate Maximum K-cutPhoto by Matt Artz on UnsplashThe 1.7 release of Neo4j’s Graph Data Science Library contains some amazing features, like machine learning... read more