Journal of Classification

Aims & Scope

To publish original and valuable papers in the field of classification, numerical taxonomy, multidimensional scaling and other ordination techniques, clustering, tree structures and other network models (with somewhat less emphasis on principal components analysis, factor analysis, and discriminant analysis), as well as associated models and algorithms for fitting them.

Articles will support advances in methodology while demonstrating compelling substantive applications.

Comprehensive review articles are also acceptable.

Contributions will represent disciplines such as statistics, psychology, biology, information retrieval, anthropology, archeology, astronomy, business, chemistry, computer science, economics, engineering, geography, geology, linguistics, marketing, mathematics, medicine, political science, psychiatry, sociology, and soil science.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.9
2024	1.80

Journal Rank

Year	Value
2024	7950

Journal Citation Indicator

Year	Value
2024	215

SJR (SCImago Journal Rank)

Year	Value
2024	0.710

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	49

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences, Mathematics, Psychology and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Comparing partitions

  Citation: 6810

  Authors: Lawrence, Phipps

• Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?

  Citation: 2814

  Authors: Fionn, Pierre

• An entropy criterion for assessing the number of clusters in a mixture model

  Citation: 1777

  Authors: Gilles, Gilda

• Efficient algorithms for agglomerative hierarchical clustering methods

  Citation: 712

  Authors: William H. E., Herbert

• Metric and Euclidean properties of dissimilarity coefficients

  Citation: 704

  Authors: J. C., P.

• Shape Statistics: Procrustes Superimpositions and Tangent Spaces

  Citation: 670

  Authors: F. James

• A study of standardization of variables in cluster analysis

  Citation: 636

  Authors: Glenn W., Martha C.

• Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method

  Citation: 503

  Authors: Gabor J., Maria L.

• Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure

  Citation: 401

  Authors: Tom A.B., Krzysztof

• A maximum likelihood methodology for clusterwise linear regression

  Citation: 380

  Authors: Wayne S., William L.

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