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    Data Mining

    Data integration & searchData pattern recognitionKnowledge discovery in data
    Editorially reviewed on: 2 sources
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    The short answer · 13 words

    In short

    Data mining systematically searches for resilient patterns and relationships in larger data stocks.

    Flow-alerta.cab (2026): Data Mining. https://flow-alerta.cab/en/glossar/data-mining. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    Data mining can support segmentation, fraud detection, quality analysis or demand planning. It provides hypotheses and models, not automatically causal or correct decisions.

    A shopping cart analysis often identifies jointly purchased product groups; a test in a later period checks whether the sample remains stable.

    SMEs start with a clear business question, testable target size and manageable data set. Results should be reviewed by experts and tested in a controlled pilot process.

    At a glance

    Core
    Data mining involves methods that derive structures, rules or predictive models from data. Typical tasks are classification, regression, clustering and association analysis. Finding patterns must be validated with suitable test data and interpreted professionally.
    Use
    Data mining can support segmentation, fraud detection, quality analysis or demand planning. It provides hypotheses and models, not automatically causal or correct decisions.
    Example
    A shopping cart analysis often identifies jointly purchased product groups; a test in a later period checks whether the sample remains stable.
    Limits
    Confuse correlation with cause, ignore data quality and selection bias, test too many hypotheses, or fail to validate results outside of training data. Data protection and purpose limitation remain relevant.

    In context

    Clustering

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

    Questions about this term

    Is data mining just SQL?

    No, queries can prepare mining modeled patterns.

    Is it machine learning?

    It overlaps, but encompasses the entire discovery and evaluation process.

    Does it have to do with cryptomining?

    No.

    All details and background

    Data mining involves methods that derive structures, rules or predictive models from data. Typical tasks are classification, regression, clustering and association analysis. Finding patterns must be validated with suitable test data and interpreted professionally.

    ELI5 – Explained simply

    You search a large data mountain for recurring traces, but then check whether they are real and useful.

    Why it matters in practice

    Data mining can support segmentation, fraud detection, quality analysis or demand planning. It provides hypotheses and models, not automatically causal or correct decisions.

    Example

    A shopping cart analysis often identifies jointly purchased product groups; a test in a later period checks whether the sample remains stable.

    Pitfalls and limits

    • Confuse correlation with cause, ignore data quality and selection bias, test too many hypotheses, or fail to validate results outside of training data. Data protection and purpose limitation remain relevant.

    Origin & history

    Data mining was disseminated in the 1990s with databases, statistics and knowledge discovery in databases. SQL/MM later standardizes data types and routines for important mining tasks.

    Use in mid-sized companies

    • SMEs start with a clear business question, testable target size and manageable data set. Results should be reviewed by experts and tested in a controlled pilot process.

    Related terms

    • Machine learning
    • Statistics
    • Clustering
    • Association analysis
    • CRISP-DM

    Matching Flow-alerta service

    AI opportunity analysis & roadmapData analysis and AI consulting: sharpening business issues, preparing data, reliably validating patterns and converting them into usable processes.

    Matching tool / use case

    View use casesPython with scikit-learn or KNIME; Use Case: Form customer segments or detect process deviations and technically test them.

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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