ACM Transactions on Knowledge Discovery from Data (TKDD) welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data.
Journal Home: Journal Homepage
Editor-in-Chief: Philip S. Yu
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.
Print ISSN: 1556-4681
Electronic ISSN: 1556-472X
Abstracting and Indexing: Science Citation Index Expanded. Scopus.
Imapct Factor 2021: 4.157
Subject Area and Category: Computer Science
Publication Frequency: Quarterly
H Index: 60
Q1: General Computer Science
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Q3:
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Cite Score: 6.0
SNIP: 2.008
Journal Rank(SJR): 1.566
Latest Articles: Latest Articles in ACM Transactions on Knowledge Discovery From Data
Guidelines for Authors: ACM Transactions on Knowledge Discovery From Data Author Guidelines
Paper Submissions: Paper Submissions in ACM Transactions on Knowledge Discovery From Data
Publisher: ACM-Association for Computing Machinery New York
Country: USA
