We just launched our courses -> Start learning today ✨

Join
Login

K-Means

ML Models & Algorithms
The most widely used centroid clustering algorithm. Efficient and scales well for large datasets.

K-Means is the most widely used example of a Hard, Centroid-based clustering algorithm. It works by partitioning data points into K clusters, aiming to minimize the within-cluster variance. The centroid of each cluster is the arithmetic mean of all points within it.
K-Means is noted for being simple to implement, efficient, and scalable for large datasets. It is suitable for numeric data and is used for customer segmentation based on numeric characteristics, market analysis, and image classification. K-Means, along with Hierarchical Clustering and DBSCAN, is a suggested algorithm for grouping competitors based on shared web performance metrics.

Related Concepts

K-Means Clustering Centroid-based Clustering Hard Clustering Distribution-based Clustering Density-based Clustering

Sources & References

Introduction to Machine Learning for SEOs Course
academy.mlforseo.com

Explore other ML Models & Algorithms terms

B
BERT (Bidirectional Encoder Representations from Transformers)
The foundational language model used for transformer-based embeddings in BERTopic.
B
BERTopic
An unsupervised machine learning approach for topic modeling that generates interpretable topics and performs dynamic…
B
BERTopic
An unsupervised machine learning approach for topic modeling that generates interpretable topics and performs dynamic…
B
BIRCH (Balanced Iterative Hierarchical Based Clustering)
A hierarchical clustering method efficient for large datasets and time series.
B
Boyer-Moore
An exact string-matching algorithm and one of the best-known pattern recognition algorithms.
C
c-TF-IDF
Class-based Term Frequency-Inverse Document Frequency; used by BERTopic for clearer topic representation and selection of…
D
DBSCAN
Density-Based Spatial Clustering of Applications with Noise; groups data points based on density. Useful for…
D
Decision Tree
An early, simple model for classification or regression.
D
Distance-based matching
Fuzzy matching methods focusing on "edit distance" rather than exact spelling.
D
DistilBERT (Refined Query Semantic Class Classifier)
A fine-tuned BERT model used for semantic class classification based on queries.
E
Encoder Model
A machine learning model used in Google's two-step process for building and maintaining the Knowledge…
F
Fuzzy Matching / Fuzzy String Matching
A string similarity assessment approach, typically relying on character distance rather than semantics, used to…