Advances in Data Analysis and Classification - Impact Factor

Advances in Data Analysis and Classification - Springer Nature | 2024 Impact Factor:1.3 | Cite Score:3.3 | Q2

Advances in Data Analysis and Classification Journal

Impact Factor and Journal Rank of Advances in Data Analysis and Classification

About: Advances in Data Analysis and Classification (ADAC) is a peer-reviewed journal that publishes high-quality research articles, review papers, and case studies in the fields of data analysis, classification, and related methods. The journal aims to provide a platform for the dissemination of significant scientific advancements in statistical data analysis, machine learning, and computational statistics.
Objective:
The journal seeks to advance the theoretical and practical understanding of data analysis and classification by promoting innovative research that develops new methodologies, enhances existing techniques, and applies these methods to real-world data. It aims to bridge the gap between theory and application, fostering the development of effective data-driven solutions.
Focus Areas:
Topics covered in Advances in Data Analysis and Classification include, but are not limited to: Statistical data analysis Machine learning and artificial intelligence Classification and clustering methods Multivariate data analysis Pattern recognition Computational statistics Data mining and knowledge discovery High-dimensional data analysis Bayesian methods Applications in bioinformatics, social sciences, economics, and engineering
Impact:
The journal significantly contributes to advancing the fields of data analysis and classification by publishing cutting-edge research that enhances methodological understanding and practical applications. It influences both academic research and practical implementations, promoting the development of advanced analytical techniques and their application to complex datasets.
Significance:
Advances in Data Analysis and Classification is significant for researchers, academics, and practitioners interested in the development and application of advanced data analysis and classification methods. It provides a comprehensive view of the latest research findings, emerging trends, and best practices in these fields, fostering collaboration and knowledge exchange within the scientific community.

Journal Home: Journal Homepage

Editor-in-Chief: Maurizio Vichi

Scope: Advances in Data Analysis and Classification covers a broad range of topics, including but not limited to:
Data Analysis: Research on methods and algorithms for analyzing data, including statistical techniques, machine learning approaches, and data mining strategies.
Classification: Studies on the development and evaluation of classification algorithms, including supervised and unsupervised learning methods, as well as applications in various domains such as bioinformatics, image processing, and text mining.
Clustering: Research on clustering techniques, including hierarchical, partitioning, and density-based methods, along with their applications in pattern recognition, market segmentation, and anomaly detection.
Dimensionality Reduction: Studies on methods for reducing the dimensionality of data, including principal component analysis, factor analysis, and manifold learning.
Visualization: Research on techniques for visualizing high-dimensional data, including interactive and dynamic visualization methods to aid in data interpretation and decision-making.
Computational Techniques: Development and application of computational algorithms for efficient data analysis and classification, including parallel and distributed computing approaches.
Applications: Case studies and practical applications of data analysis, classification, and clustering techniques in various fields such as healthcare, finance, marketing, and social sciences.
Theoretical Foundations: Research on the mathematical and statistical foundations of data analysis, classification, and clustering methods, including theoretical guarantees and performance analysis.