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 2020:  2.010
Subject Area and Category:  Computer Science
Publication Frequency:  Quarterly
H Index:  59
Q1:  General Computer Science
Cite Score:  4.3
Journal Rank(SJR):  0.728
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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