Data Mining and Knowledge Discovery

  in Journal   Posted on September 23, 2020

Journal Ranking & Metrics

G2R Score : 4.12
G2R H-Index : 15
JCR Impact Factor : 2.629
Scopus Citescore : 7.4
SCIMAGO SJR : 1.052
SCIMAGO H-index : 96
Guide2Research Overall Ranking : 224

Journal Information

ISSN : 1384-5810
Publisher :
Periodicity : Bimonthly
Editors-in-Chief : Johannes Fürnkranz
Journal & Submission Website : https://www.springer.com/journal/10618

Top Scientists who published in this Journal

Number of top scientists* : 41
Documents published by top scientists* : 53
* Based on data published during the last three years.

Aims & Scope of the Journal

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. KDD is concerned with issues of scalability, the multi-step knowledge discovery process for extracting useful patterns and models from raw data stores (including data cleaning and noise modelling), and issues of making discovered patterns understandable. Data Mining and Knowledge Discovery is the premier technical publication in the field, providing a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of DMKD, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications. Short (2-4 pages) application summaries are published in a special section. The journal accepts paper submissions of any work relevant to DMKD. A summary of the scope of Data Mining and Knowledge Discovery includes:Theory and Foundational Issues: Data…