K-means
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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.
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.
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.
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K-means Complementary Technologies
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Which job functions commonly mention K-means?
Data Scientist
0.2% of all Data Scientist jobs mention K-means
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jobs on Sumble
AI Engineer
0.1% of all AI Engineer jobs mention K-means
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jobs on Sumble
Machine Learning
0.1% of all Machine Learning jobs mention K-means
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jobs on Sumble
Data, Analytics & Machine Learning
773 Data, Analytics & Machine Learning jobs mention K-means
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jobs on Sumble
Data Analyst
83 Data Analyst jobs mention K-means
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jobs on Sumble
Which organizations are mentioning K-means?
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