What question did this study set out to answer?

Review and classify distance-based clustering algorithms tailored for datasets containing both continuous and categorical variables.

November 5, 2018Wiley Interdisciplinary Reviews Computational StatisticsOpen Access

Distance‐based clustering of mixed data

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Authors

MVMichel van de VeldenErasmus University Rotterdam ADAlfonso Iodice D’EnzaUniversity of Naples Federico II AMAngelos MarkosDemocritus University of Thrace

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Implication

Methodological review outlines approaches for distance-based clustering in mixed data, highlighting preprocessing, specialized metrics, and joint dimension reduction.

Key Points

Review and classify distance-based clustering algorithms tailored for datasets containing both continuous and categorical variables.
Synthesized unsupervised distance-based clustering techniques adapted to mixed-type data structures.
Categorized existing analytical approaches into three primary methodological streams based on their data handling and clustering strategies.
Identified data preprocessing as a primary approach where mixed variables are scaled or converted to a uniform scale prior to clustering.
Determined specialized distance metrics tailored to simultaneously evaluate continuous and categorical features directly.
Characterized joint data reduction frameworks that combine dimension reduction with clustering routines specifically for mixed-type datasets.

Cite This Study

Velden et al. (2018) studied this question.

synapsesocial.com/papers/6a1055338090e499da60fed8 https://doi.org/10.1002/wics.1456

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Implication

Methodological review outlines approaches for distance-based clustering in mixed data, highlighting preprocessing, specialized metrics, and joint dimension reduction.

Distance‐based clustering of mixed data

Discussion

Member takes

Implication

Key Points

Cite This Study

Distance‐based clustering of mixed data

Discussion

Member takes

Implication

Key Points

Cite This Study

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