We just launched our courses -> Start learning today ✨

Join
Login

Soft/Fuzzy Clustering

Task Types
A type of clustering where data points can belong to multiple topics/clusters with varying probabilities (e.g., LDA).

Soft clustering (or fuzzy clustering) is a probabilistic approach where each data point or document is assigned a probability of belonging to multiple segments or topics. This approach reflects the likelihood of association with each segment, capturing the nuance present in natural language.
Soft clustering is recommended when customer behavior overlaps between segments, or when probabilistic insights are needed to create tailored campaigns and identify multi-segment opportunities, such as cross-sell possibilities. BERTopic is explicitly classified as a Soft/Fuzzy Clustering model, enabling documents to be assigned to multiple topics or near topic boundaries.

Related Concepts

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

Sources & References

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

Explore other Task Types terms

B
Binary Classification
Classification task with two possible outcomes (e.g., positive or negative sentiment).
C
Centroid-based Clustering
Organizes data into non-hierarchical clusters based on the arithmetic mean (centroid) of the points. Efficient…
C
Clustering (ML Task)
Grouping data points into clusters based on similarity; an unsupervised learning task.
D
Density-based Clustering
Groups data points based on density and proximity. Does not require pre-defining the number of…
D
Distribution-based Clustering
Assumes data is composed of probabilistic distributions (e.g., Gaussian Mixture Model).
H
Hard Clustering
A type of clustering where data points are assigned exclusively to a single cluster.
H
Hierarchical Clustering
A clustering approach where data points are recursively merged or split to create a tree-like…
M
Multi-Class Classification
Classification where data is assigned exclusively to one of three or more options (e.g., categorizing…
M
Multi-Label Classification
Classification where an input can belong to multiple categories simultaneously (e.g., tagging a blog post…