Data Mining and Knowledge Discovery

Aims & Scope

Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis.

Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	4.3
2024	2.80

SJR (SCImago Journal Rank)

Year	Value
2024	1.019

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	123

Journal Rank

Year	Value
2024	4667

Journal Citation Indicator

Year	Value
2024	1162

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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 Computer Science, 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.

• A Tutorial on Support Vector Machines for Pattern Recognition

  Citation: 11670

  Authors: Christopher J.C.

• Deep learning for time series classification: a review

  Citation: 2370

  Authors: Hassan, Germain, Jonathan, Lhassane, Pierre-Alain

• Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

  Citation: 1880

  Authors: Jiawei, Jian, Yiwen, Runying

• Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

  Citation: 1862

  Authors: Zhexue

• Experiencing SAX: a novel symbolic representation of time series

  Citation: 1217

  Authors: Jessica, Eamonn, Li, Stefano

• InceptionTime: Finding AlexNet for time series classification

  Citation: 1108

  Authors: Hassan, Benjamin, Germain, Charlotte, Daniel F., Jonathan, Geoffrey I., Lhassane, Pierre-Alain, François

• Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

  Citation: 1104

  Authors: Jörg, Martin, Hans-Peter, Xiaowei

• Graph based anomaly detection and description: a survey

  Citation: 1047

  Authors: Leman, Hanghang, Danai

• The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

  Citation: 1041

  Authors: Anthony, Jason, Aaron, James, Eamonn

• Frequent pattern mining: current status and future directions

  Citation: 963

  Authors: Jiawei, Hong, Dong, Xifeng

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