A type of clustering where data points are assigned exclusively to a single cluster.
Hard clustering is a clustering approach where each customer or data point is assigned exclusively to a single segment or cluster. The resulting segments are non-overlapping; a data point belongs either to one cluster or the other, based on specific criteria like purchase history or demographics.
This method is best employed when customer segments are definitively distinct and marketing strategies need to target non-overlapping groups, such as running specific loyalty programs. K-Means and DBSCAN are examples of hard clustering algorithms.
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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
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…
S
Soft/Fuzzy Clustering
A type of clustering where data points can belong to multiple topics/clusters with varying probabilities…