Wiley Interdisciplinary Reviews-data Mining - Impact Factor

Wiley Interdisciplinary Reviews-data Mining and Knowledge Discovery | 2024 Impact Factor:11.7 | Cite Score:21.7 | Q1

Wiley Interdisciplinary Reviews-data Mining and Knowledge Discovery

About: The Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (WIREs DMKD) journal is a prominent academic publication that focuses on the interdisciplinary aspects of data mining and knowledge discovery. It aims to provide comprehensive, high-quality reviews and articles that synthesize the latest advancements and methodologies in the field. By bridging the gap between theory and practice, the journal serves as a crucial resource for researchers, practitioners, and students who are involved in the exploration and analysis of large datasets to extract meaningful patterns and insights.
Content Types: Review Articles: Comprehensive reviews that provide an overview of the current state of research in specific areas of data mining and knowledge discovery, summarizing key findings and identifying future research directions. Original Research Articles: Detailed reports on original research, presenting novel methodologies, experimental results, and theoretical advancements in the field. Case Studies: Practical case studies demonstrating the application of data mining and knowledge discovery techniques in real-world scenarios, highlighting challenges and solutions.
High Standards and Impact: Peer-Reviewed: WIREs DMKD employs a rigorous peer-review process to ensure the quality, accuracy, and significance of published articles. Interdisciplinary Approach: The journal interdisciplinary approach encourages the integration of knowledge from various fields, fostering innovation and collaboration. Citation Impact: Articles published in WIREs DMKD are widely cited, reflecting the journal influence and contribution to the advancement of data mining and knowledge discovery research.

Global Reach: International Contributors: The journal attracts contributions from researchers and practitioners worldwide, providing diverse perspectives and insights. Multidisciplinary Content: WIREs DMKD publishes research that spans multiple disciplines, promoting a holistic understanding of data mining and its applications.
Significance: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery journal is a leading publication that significantly contributes to the advancement of data mining and knowledge discovery. Its interdisciplinary focus, high standards, and global reach make it an invaluable resource for researchers, practitioners, policymakers, and students in the field.

Journal Home: Journal Homepage

Editor-in-Chief: Witold Pedrycz

Scope: The journal aims to bridge the gap between various disciplines and facilitate the exchange of knowledge among researchers, practitioners, and educators in the field.

The scope of the journal includes, but is not limited to, the following areas:
Data Mining Techniques: Coverage includes various data mining methods such as classification, clustering, regression, association rule mining, anomaly detection, and sequential pattern mining.
Knowledge Discovery: Focus on the process of discovering useful information and patterns from data, including preprocessing, transformation, feature selection, and model evaluation.
Machine Learning and AI: Integration of machine learning and artificial intelligence techniques in data mining, including supervised, unsupervised, and reinforcement learning methods.
Big Data Analytics: Exploration of techniques and tools for handling and analyzing big data, including distributed computing frameworks, parallel processing, and scalable algorithms.
Applications: Application of data mining and knowledge discovery in various domains such as healthcare, finance, marketing, bioinformatics, cybersecurity, and social networks.
Ethical and Social Implications: Discussions on the ethical, legal, and social issues related to data mining, including privacy, data security, bias, and fairness.
Emerging Trends: Insights into emerging trends and future directions in data mining and knowledge discovery, including deep learning, real-time analytics, and the Internet of Things (IoT).