Statistical Analysis and Data Mining Wiley-Blackwell Journal Impact Factor

Statistical Analysis and Data Mining - John Wiley and Sons | 2024 Impact Factor:2.1 | Cite Score:4.0 | Q3

Statistical Analysis and Data Mining Journal With Cite Score

Cite Score and Journal Rank of Statistical Analysis and Data Mining

About: The Statistical Analysis and Data Mining Journal is a peer-reviewed academic journal dedicated to the dissemination of research in the field of statistical analysis, data mining, and related areas. The journal focuses on publishing high-quality papers that present new methodologies, theoretical advancements, and applications in the analysis of large and complex datasets. It serves as a forum for both theoretical and practical contributions that advance the understanding and utilization of statistical and data mining techniques.
Objective:
The primary objective of the Statistical Analysis and Data Mining Journal is to promote the development and application of statistical methods and data mining techniques. The journal aims to bridge the gap between theoretical research and practical applications by publishing articles that provide innovative solutions to real-world problems. It seeks to foster the exchange of ideas and methodologies among researchers, practitioners, and policymakers in various fields such as finance, healthcare, engineering, and social sciences.
Interdisciplinary Approach:
The journal adopts an interdisciplinary approach, welcoming contributions from a wide range of disciplines including statistics, computer science, mathematics, engineering, and the natural and social sciences. This multidisciplinary perspective ensures that the journal covers diverse methodologies and applications, facilitating a comprehensive understanding of statistical analysis and data mining. The journal encourages the integration of different techniques and theories to address complex data challenges effectively.
Impact:
The Statistical Analysis and Data Mining Journal has a significant impact on both the academic community and industry. Its articles are widely cited and serve as key references for researchers and practitioners in the field. The journals publications contribute to the advancement of knowledge in statistical analysis and data mining, providing new insights and methodologies that are applicable across various domains. The practical applications of the research published in the journal help organizations and policymakers make informed decisions based on data-driven insights.
Significance:
The Statistical Analysis and Data Mining Journal is significant for its role in advancing the field of data analysis. It provides a platform for the dissemination of cutting-edge research and fosters collaboration among researchers from different disciplines. The journals focus on both theoretical and practical contributions ensures that it remains relevant and influential in the rapidly evolving field of data science. By publishing innovative research, the journal helps to shape the future of statistical analysis and data mining, contributing to the development of more effective and efficient data-driven solutions.

Journal Home: Journal Homepage

Editor-in-Chief: Cinzia Viroli

Scope: Statistical Analysis and Data Mining (SADM) is a peer-reviewed journal that focuses on the application of statistical analysis and data mining techniques in various fields. The journal aims to bridge the gap between theory and practice by providing a platform for the dissemination of innovative research and practical applications. Its scope includes, but is not limited to, the following areas:
Statistical Methods: Research on new statistical methodologies, including theoretical developments and practical applications. Topics include regression analysis, multivariate analysis, time series analysis, and statistical inference.
Data Mining Techniques: Studies on data mining algorithms and techniques for discovering patterns and insights from large datasets. Topics include clustering, classification, association rule mining, and anomaly detection.
Machine Learning: Research on machine learning algorithms and their applications in various domains. Topics include supervised and unsupervised learning, reinforcement learning, deep learning, and neural networks.
Big Data Analytics: Studies on the analysis of large and complex datasets using advanced statistical and data mining techniques. Topics include big data frameworks, scalable algorithms, and real-time analytics.
Predictive Modeling: Research on predictive modeling techniques for forecasting and decision-making. Topics include predictive analytics, time series forecasting, and risk modeling.
Data Visualization: Studies on methods and tools for visualizing complex data and statistical results. Topics include interactive visualizations, dashboards, and graphical representations of data.
Applications in Various Domains: Research on the application of statistical analysis and data mining techniques in different fields such as healthcare, finance, marketing, social sciences, engineering, and environmental science.
Case Studies and Practical Implementations: Practical examples and case studies demonstrating the application of statistical and data mining techniques to solve real-world problems.