ACM Transactions on Knowledge Discovery from Data

  in Journal   Posted on September 23, 2020

Journal Ranking & Metrics

G2R Score : 5.78
G2R H-Index : 15
JCR Impact Factor : 2.01
Scopus Citescore : 4.3
SCIMAGO H-index : 49
Guide2Research Overall Ranking : 136

Journal Information

ISSN : 1556-4681
Publisher :
Periodicity : Quarterly
Editors-in-Chief : Xindong Wu, Charu C. Aggarwal
Journal & Submission Website :

Top Scientists who published in this Journal

Number of top scientists* : 62
Documents published by top scientists* : 71
* Based on data published during the last three years.

Aims & Scope of the Journal

The 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. Such subjects include: scalable and effective algorithms for data mining and data warehousing, 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. 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.

Closed Special Issues



ACM Transactions on Knowledge Discovery from Data
Wed 26 Aug 2015