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    Clustering

    AI fundamentals & modelsCluster analysisGroupingUnsupervised clustering
    Editorially reviewed on: 1 source
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    The short answer · 20 words

    In short

    Unsupervised learning method that divides data points into groups based on their similarity or structure, without requiring predefined class labels.

    Flow-alerta.cab (2026): Clustering. https://flow-alerta.cab/en/glossar/clustering. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Relevant for segmentation, exploratory data analysis, document groups, customer patterns and pre-processing.

    Example scenario: Support requests are grouped based on their text embeddings to discover recurring themes.

    First, define goal, representation and similarity measure; technically examine found groups and examine stability over different parameters.

    At a glance

    Core
    Clustering tries to find groups in data so that elements within a cluster are as similar as possible according to a chosen similarity measure. Methods such as k-means, hierarchical clustering or DBSCAN use different assumptions about the shape, density and number of groups. Clusters are mathematical structures and not automatically technically meaningful segments.
    Use
    Relevant for segmentation, exploratory data analysis, document groups, customer patterns and pre-processing.
    Example
    Example scenario: Support requests are grouped based on their text embeddings to discover recurring themes.
    Limits
    Automatically interpret clusters as real customer segments; ignore scaling of features; set k without checking; select procedures based only on a single score.

    In context

    DBSCAN

    DBSCAN finds dense groups of any shape and identifies isolated data points as noise.

    Embedding

    An embedding maps content as a number vector to compare similarities.

    Chunking

    Dividing longer documents or texts into smaller, usefully processable sections for search, embeddings or RAG.

    Questions about this term

    Does clustering need labels?

    No.

    Is K-Means the only procedure?

    No.

    Are found clusters automatically business-wise?

    No.

    All details and background

    Clustering tries to find groups in data so that elements within a cluster are as similar as possible according to a chosen similarity measure. Methods such as k-means, hierarchical clustering or DBSCAN use different assumptions about the shape, density and number of groups. Clusters are mathematical structures and not automatically technically meaningful segments.

    ELI5 – Explained simply

    The method independently searches for groups of data points that are more similar to each other than the other data.

    Why it matters in practice

    Relevant for segmentation, exploratory data analysis, document groups, customer patterns and pre-processing.

    Example

    Example scenario: Support requests are grouped based on their text embeddings to discover recurring themes.

    Pitfalls and limits

    • Automatically interpret clusters as real customer segments; ignore scaling of features; set k without checking; select procedures based only on a single score.

    Origin & history

    Cluster analysis is a classic field of statistics and unsupervised machine learning with numerous methods and distance measures.

    Use in mid-sized companies

    • First, define goal, representation and similarity measure; technically examine found groups and examine stability over different parameters.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: AI potential analysis & roadmap on tasks, data and prerequisites of a possible first AI use case.

    Matching tool / use case

    View use casesUse Case: thematic grouping of text embeddings or customer data.

    Sources

    Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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