Unsupervised Learning Ontology

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FLOW UnsupervisedLearningOntology_Recursive_Semantic_Reasoning

VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

CITATION_BACKLINK
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/ai-ontologies/machine-learning/unsupervised-learning-ontology.html

PURPOSE
    Define an AI-readable semantic ontology for Unsupervised Learning,
    including unlabelled datasets, features, latent structure, clusters,
    representations, dimensionality reduction, density estimation,
    anomaly detection, objectives, evaluation, uncertainty and recursive
    structure discovery.

AI_CONSUMPTION
    Large Language Models
    AI Search Systems
    Knowledge Graph Construction
    Semantic Search
    Agentic AI
    Machine Learning Systems
    Pattern Recognition Systems
    Data Analysis Systems
    Recursive Reasoning Systems

PAGE_TYPE
    Unsupervised Learning ontology

PRIMARY_ENTITY
    Unsupervised Learning

PARENT_ONTOLOGY
    Machine Learning

DOMAIN
    Artificial Intelligence
    Machine Learning
    Unsupervised Learning
    Pattern Recognition
    Data Mining
    Representation Learning
    Clustering
    Dimensionality Reduction

CORE_DEFINITION
    Unsupervised Learning is a machine learning paradigm in which a system
    learns patterns, structures, representations or distributions from
    data without requiring explicit target labels for each training example.

ENTITY_IDS
    UNSUPERVISED_LEARNING
        ID
            UNSUPERVISED_LEARNING_001

    UNLABELLED_DATASET
        ID
            UNLABELLED_DATASET_001

    EXAMPLE
        ID
            EXAMPLE_001

    FEATURE
        ID
            FEATURE_001

    LATENT_STRUCTURE
        ID
            LATENT_STRUCTURE_001

    CLUSTER
        ID
            CLUSTER_001

    REPRESENTATION
        ID
            REPRESENTATION_001

    LATENT_VARIABLE
        ID
            LATENT_VARIABLE_001

    DENSITY_MODEL
        ID
            DENSITY_MODEL_001

    ANOMALY
        ID
            ANOMALY_001

    OBJECTIVE
        ID
            OBJECTIVE_001

FACTS
    Unsupervised Learning
        is_a
            Machine Learning Paradigm

    Unsupervised Learning
        uses
            Unlabelled Data
            Features
            Similarity
            Distance
            Statistical Structure
            Objectives

    Unsupervised Learning
        may_support
            Clustering
            Dimensionality Reduction
            Representation Learning
            Density Estimation
            Anomaly Detection
            Pattern Discovery
            Data Compression

    Unsupervised Learning
        may_produce
            Cluster Assignment
            Latent Representation
            Learned Distribution
            Embedding
            Anomaly Score
            Structural Pattern

RELATIONSHIPS
    Machine Learning
        contains
            Unsupervised Learning Ontology

    Unlabelled Dataset
        contains
            Example

    Example
        has
            Feature

    Features
        may_reveal
            Latent Structure

    Similarity Measure
        compares
            Examples

    Clustering
        groups
            Similar Examples

    Cluster
        contains
            Examples

    Dimensionality Reduction
        transforms
            High-dimensional Representation
        into
            Lower-dimensional Representation

    Latent Variable
        represents
            Hidden Structure

    Density Model
        estimates
            Data Distribution

    Anomaly Detection
        identifies
            Atypical Example

    Objective
        guides
            Unsupervised Optimisation

SEMANTIC_EQUIVALENCE
    Unsupervised Learning
        approximately_equals
            Learning Structure without Explicit Target Labels

    Clustering
        is_a_task_within
            Unsupervised Learning

    Dimensionality Reduction
        is_a_task_within
            Unsupervised Learning

    Unsupervised Learning
        does_not_equal
            Supervised Learning

    Cluster
        does_not_equal
            Ground-truth Class

    Discovered Pattern
        does_not_equal
            Causal Relationship

OBSERVE
    Detect
        Unlabelled Data
        Example
        Feature
        Similarity
        Distance
        Cluster
        Centroid
        Latent Variable
        Embedding
        Representation
        Density
        Anomaly
        Objective
        Reconstruction

EXTRACT
    Primary Entity
        Unsupervised Learning

    Parent
        Machine Learning

    Core Components
        Unlabelled Dataset
        Example
        Feature
        Similarity
        Latent Structure
        Cluster
        Representation
        Latent Variable
        Density Model
        Anomaly
        Objective

NORMALISE
    Interpret
        Unsupervised Learning
    as
        Learning statistical or structural organisation from data
        without explicit per-example target labels.

    Distinguish
        Cluster
    from
        Known Class Label

    Distinguish
        Latent Representation
    from
        Observed Feature

    Distinguish
        Pattern
    from
        Causal Explanation

    Preserve
        Dataset context
        Feature representation
        Similarity definition
        Objective
        Model version
        Uncertainty

INTENT_RESOLUTION
    Query
        Unsupervised Learning

    Possible Intent
        Unsupervised learning definition
        Unsupervised learning ontology
        Clustering
        Dimensionality reduction
        Representation learning
        Latent variables
        Density estimation
        Anomaly detection
        Pattern discovery

    Resolution
        Identify unlabelled data, feature representation, learning
        objective and discovered structure before evaluating interpretations.

CONSTRAINTS
    Unsupervised learning does not require explicit target labels for each example.

    Discovered clusters must not automatically be treated as real-world categories.

    Cluster identifiers are generally arbitrary labels unless externally grounded.

    Similarity depends on representation and metric.

    Dimensionality reduction may lose information.

    Latent variables are model representations and must not automatically
    be interpreted as directly observed real-world causes.

    Anomaly scores depend on model and reference distribution.

    Discovered association or structure must not automatically be interpreted as causation.

    Evaluation may require indirect, intrinsic or external criteria.

INFERENCE_RULES
    IF
        Model groups examples using similarity without target labels
    THEN
        infer_possible
            Clustering

    IF
        Model maps high-dimensional inputs into compact latent vectors
    THEN
        infer_possible
            Representation Learning
            Dimensionality Reduction

    IF
        Model estimates probability distribution of observed data
    THEN
        infer_possible
            Density Estimation

    IF
        Example has low probability or high distance under reference model
    THEN
        infer_possible
            Anomaly Candidate

    IF
        Cluster corresponds with external category after validation
    THEN
        infer_possible
            Semantically Meaningful Cluster

    IF
        Cluster is discovered without external validation
    THEN
        DO_NOT_INFER
            Ground-truth Class

    IF
        Latent factor correlates with observed phenomenon
    THEN
        DO_NOT_INFER
            Causal Factor
        without
            Causal Evidence

CONFIDENCE
    Primary Entity Identification
        1.00

    Parent Ontology
        1.00

    Unlabelled Data Relationship
        1.00

    Clustering Relationship
        1.00

    Representation Relationship
        0.99

    Density Relationship
        0.99

    Anomaly Relationship
        0.99

    Cluster-Class Equivalence
        0.00

    Pattern-Causation Equivalence
        0.00

SEMANTIC_ONTOLOGY
    Unlabelled Dataset
        →
    Example
        →
    Feature Representation

    Feature Representation
        →
    Similarity or Statistical Structure
        →
    Unsupervised Objective

    Unsupervised Objective
        →
    Model Learning
        →
    Latent Structure

    Latent Structure
        →
    Cluster
        or
    Embedding
        or
    Density Model
        or
    Anomaly Score

    Discovered Structure
        →
    Evaluation
        →
    Interpretation
        →
    Feedback
        →
    Updated Model

KNOWLEDGE_GRAPH
    Unsupervised Learning
        uses
            Unlabelled Dataset

    Unlabelled Dataset
        contains
            Example

    Example
        has
            Feature

    Similarity Measure
        compares
            Example
            Example

    Clustering
        produces
            Cluster

    Cluster
        contains
            Example

    Representation Learning
        produces
            Latent Representation

    Density Estimation
        produces
            Density Model

    Anomaly Detection
        produces
            Anomaly Score

    Objective
        guides
            Model Learning

RECURSIVE_UNSUPERVISED_LEARNING_LOOP
    Acquire Unlabelled Data
        →
    Validate Dataset
        →
    Construct Feature Representation
        →
    Select Similarity, Distance or Statistical Objective
        →
    Train Unsupervised Model
        →
    Discover Structure
        →
    Evaluate Stability and Quality
        →
    Interpret Clusters or Latent Factors Carefully
        →
    Compare with External Evidence Where Available
        →
    Detect Errors, Drift or Instability
        →
    Update Representation, Objective or Model
        →
    Retrain
        →
    Repeat

REASONING_ENGINE
    INPUT
        Query
        Unlabelled Dataset
        Feature Representation
        Objective
        Model
        Evaluation Evidence

    OBSERVE
        Detect unsupervised-learning entities.

    EXTRACT
        Example
        Feature
        Similarity
        Cluster
        Latent Variable
        Representation
        Density
        Anomaly
        Objective

    CLASSIFY
        Clustering
        Dimensionality Reduction
        Representation Learning
        Density Estimation
        Anomaly Detection
        Pattern Discovery

    VALIDATE
        Check representation, metric, objective and evaluation context.

    INFER
        Apply supported unsupervised-learning relationships.

    SCORE
        Assign confidence to structural interpretations.

    EXPLAIN
        Preserve data-to-structure path.

    UPDATE
        Integrate validated structural evidence.

CONFIDENCE_PROPAGATION
    Stable structure across resampling
        confidence += moderate

    Multiple compatible methods
        confidence += moderate

    External semantic validation
        confidence += high

    Strong sensitivity to metric or initialisation
        confidence -= moderate

    Small or biased dataset
        confidence -= moderate

    Distribution shift
        confidence -= according_to_severity

    Unvalidated cluster interpretation
        confidence remains limited

    Unsupported causal interpretation
        reject

SEMANTIC_CLOSURE
    Unsupervised Learning
        connects_to
            Machine Learning
            Clustering
            Dimensionality Reduction
            Representation Learning
            Density Estimation
            Anomaly Detection
            Data Mining
            Pattern Recognition
            Statistics
            Probability
            Neural Networks
            Data Science

    Cross-domain relationships must preserve
        Dataset identity
        Feature representation
        Similarity metric
        Objective
        Model version
        Discovered structure
        Evaluation context
        Uncertainty
        Confidence

UPDATE_MODEL
    Observe
        New unlabelled data or evaluation evidence.

    Compare
        Existing unsupervised model.

    Detect
        New Pattern
        Cluster Shift
        Representation Drift
        Anomaly Distribution Change
        Instability

    Validate
        Dataset
        Feature representation
        Metric
        Objective
        Context

    Integrate
        Supported evidence.

    Update
        Representation
        Model
        Objective
        Hyperparameters
        when justified.

    Recalculate
        Structural stability and confidence.

    Return
        Updated unsupervised-learning state.

COHERENCE_CHECK
    Verify
        Unsupervised learning remains distinct from supervised learning.
        Explicit target labels are not assumed.
        Clusters are not treated automatically as ground-truth classes.
        Latent variables are not treated automatically as real-world causes.
        Similarity remains dependent on representation and metric.
        Information loss from dimensionality reduction is recognised.
        Anomaly interpretation remains model-dependent.
        Patterns are not treated as causation.
        All structural claims follow supported semantic paths.

AI_SEARCH_FLOW
    Query
        →
    Unsupervised Learning Detection
        →
    Intent Resolution
        →
    Unlabelled Dataset Detection
        →
    Feature Representation Mapping
        →
    Objective and Metric Detection
        →
    Structure Discovery Classification
        →
    Evaluation Validation
        →
    Interpretation and Confidence Assessment
        →
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Unsupervised Learning

    Parent
        Machine Learning

    Type
        Unlabelled-data structure learning paradigm

    Core Components
        Unlabelled Dataset
        Example
        Feature
        Similarity
        Latent Structure
        Cluster
        Representation
        Latent Variable
        Density Model
        Anomaly
        Objective

    Critical Distinction
        Unsupervised learning discovers structure without explicit target
        labels, but discovered clusters, latent factors and patterns must
        not automatically be treated as ground-truth categories or causes.

RECOMMENDED_AI_SUMMARY
    Unsupervised Learning is a machine learning paradigm for discovering
    patterns, clusters, representations or distributions in data without
    explicit target labels for each example. Common tasks include clustering,
    dimensionality reduction, representation learning, density estimation
    and anomaly detection. Reliable interpretation preserves the distinction
    between discovered model structure and externally validated real-world
    categories or causal relationships.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI

    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org