K-means Insights

What is K-means?

K-means clustering is an unsupervised learning algorithm used to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. It's commonly used for customer segmentation, anomaly detection, and image compression.

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What other technologies are related to K-means?

K-means Competitor Technologies

hierarchical clustering

Hierarchical clustering is an alternative clustering algorithm that, unlike K-means, builds a hierarchy of clusters.

mentioned alongside K-means in 58% (124) of relevant job posts

DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that can discover clusters of arbitrary shape, which is a different approach than K-means.

mentioned alongside K-means in 39% (126) of relevant job posts

K-means Complementary Technologies

PCA

Principal Component Analysis is a dimensionality reduction technique that can be used to preprocess data for K-means, making K-means faster and more effective.

mentioned alongside K-means in 4% (129) of relevant job posts