k-means clustering
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What is k-means clustering?
K-means clustering is an unsupervised machine learning algorithm used to partition n data points into k clusters, where each data point belongs to the cluster with the nearest mean (centroid). It is commonly used for data analysis, customer segmentation, and anomaly detection.
What other technologies are related to k-means clustering?
k-means clustering Competitor Technologies
neighbors algorithms
No summary available
Clustering methods like k-Nearest Neighbors can achieve similar goals as k-means, grouping data points based on proximity.
hierarchical/agglomerative clustering
No summary available
Hierarchical clustering provides an alternative approach to grouping data points into clusters, without requiring a pre-defined number of clusters like k-means.
DBSCAN
DBSCAN is a density-based clustering algorithm, offering an alternative approach to k-means, especially when clusters have non-spherical shapes.
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k-means clustering Complementary Technologies
PCA
PCA (Principal Component Analysis) can be used as a pre-processing step to reduce the dimensionality of the data before applying k-means.
scikit-learn
Scikit-learn is a Python library that provides implementations of various machine learning algorithms, including k-means.
NumPy
NumPy is a Python library that provides support for arrays and mathematical operations, which are useful for implementing k-means.
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Which job functions commonly mention k-means clustering?
Data Scientist
0.1% of all Data Scientist jobs mention k-means clustering
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jobs on Sumble
Applied Scientist
5 Applied Scientist jobs mention k-means clustering
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jobs on Sumble
Machine Learning
26 Machine Learning jobs mention k-means clustering
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jobs on Sumble
AI Engineer
10 AI Engineer jobs mention k-means clustering
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jobs on Sumble
Data Analyst
47 Data Analyst jobs mention k-means clustering
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jobs on Sumble
Which organizations are mentioning k-means clustering?
Accenture
Professional Services
Accenture in India
Professional Services
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