ACM Transactions on Knowledge Discovery from Data

Aims & Scope

TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data.

Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms.

TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices.

TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	4.8

Journal Rank

Year	Value
2024	3625

Journal Citation Indicator

Year	Value
2024	2454

SJR (SCImago Journal Rank)

Year	Value
2024	1.186

Quartile

Year	Value
2024	Q1

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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.

• <i>L</i> -diversity

  Citation: 2167

  Authors: Ashwin, Daniel, Johannes, Muthuramakrishnan

• Graph evolution

  Citation: 1938

  Authors: Jure, Jon, Christos

• Isolation-Based Anomaly Detection

  Citation: 1458

  Authors: Fei Tony, Kai Ming, Zhi-Hua

• Clustering high-dimensional data

  Citation: 790

  Authors: Hans-Peter, Peer, Arthur

• Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

  Citation: 582

  Authors: Ricardo J. G. B., Davoud, Arthur, Jörg

• Clustering aggregation

  Citation: 580

  Authors: Aristides, Heikki, Panayiotis

• Factor in the neighbors

  Citation: 527

  Authors: Yehuda

• Collective entity resolution in relational data

  Citation: 381

  Authors: Indrajit, Lise

• Knowledge Graph Embedding for Link Prediction

  Citation: 371

  Authors: Andrea, Denilson, Donatella, Antonio, Paolo

• Leakage in data mining

  Citation: 366

  Authors: Shachar, Saharon, Claudia, Ori

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