Clustering

As explained in Inside AI

Clustering is the computational task of grouping data items according to similarities without requiring predefined categories.

How Inside AI covers it

How deep it goes

Defines grouping and illustrates applications, but does not explain specific algorithms, implementation steps, or evaluation methods.

Examples

Uses retail customer groups, population-based city groups, and financial-transaction analysis as concrete applications.

What it assumes

Assumes data items can be compared for similarity, while introducing the absence of labeled outputs and predefined categories.

What you can do after

Apply clustering to segment customers, group cities, and explore patterns in unlabeled or unorganized data.

How it connects

Connects clustering with unsupervised learning, data transformation, data normalization, customer segmentation, and pattern detection.

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Also explained in

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  Data Without LabelsClustering is an unsupervised technique that groups data points so that members of the same group are more similar to one another than to members of other groups.
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  The Well-Grounded Data AnalystClustering is the process of grouping observations so that members of the same group are more similar to one another than to members of other groups.
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  Outlier Detection in PythonAn unsupervised learning technique that divides items into groups whose members are more similar to one another than to members of other groups.
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  Advanced Algorithms and Data StructuresA clustering algorithm groups unlabeled data points into subsets according to relationships or similarities among them.
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  Grokking Machine LearningAn algorithm that partitions data points into groups based on similarity.
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  The Art of AI Product DevelopmentClustering is an unsupervised learning technique that groups data points according to similarities in their characteristics.
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  Statistics Slam DunkCustomer segmentation is the division of customers into groups based on shared purchasing, demographic, or other attributes.
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  The AI Pocket BookAn unsupervised learning method that groups similar data points into clusters.
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  First-Party Data ActivationThe practice of dividing a customer or user population into groups with shared characteristics or behaviors for differentiated marketing.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsThe task of partitioning objects into groups whose members are more similar to one another than to members of other groups.
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  Classic Computer Science Problems in Swift: Essential techniques for practicing programmersClustering is a computational technique that divides data points into groups whose members are related according to some measure or interpretation.
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  AI-Powered SearchClustering is an unsupervised learning technique that groups items according to similarity.
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  How Large Language Models WorkA technique that groups data items according to similarities among their features or representations.
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  Data Analysis with LLMsText clustering is the unsupervised grouping of text documents according to the similarity of their represented features.
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  Data-Driven CybersecurityClustering is a data-analysis technique that groups observations according to similarity without requiring predefined labels.
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  Learn Docker in a Month of Lunches, Second EditionA cluster is a group of machines managed together as a single computing system.
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  Financial AI in PracticeClustering is the grouping of observations according to similarities in their measured attributes.

Compare how 18 books explain clustering →

Related concepts

• K-means clusteringproduced by · K-means clustering partitions data into a specified number of groups by assigning observations to representative centers and iteratively updating those centers to reduce within-group distances.
• k-means++ initializationincludes · K-means++ initialization is a procedure that selects initial centroids using probability proportional to squared distance from previously selected centroids.
• unsupervised learningkind of · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• DBSCANincludes · DBSCAN is a density-based clustering algorithm that groups observations in dense regions and identifies observations in lower-density regions as noise or outliers.
• hierarchical clusteringincludes · Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
• embeddingused by · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.