Data Mining and Knowledge Discovery - Springer - Impact Factor

Data Mining and Knowledge Discovery - Springer | 2024 Impact Factor:4.3 | Cite Score:8.2 | Q1

Data Mining and Knowledge Discovery Journal

Impact Factor and Journal Rank of Data Mining and Knowledge Discovery

About: Data Mining and Knowledge Discovery is a peer-reviewed journal published by Springer. It focuses on research in the field of data mining and knowledge discovery, providing a platform for researchers, academics, and practitioners to publish original research articles, reviews, and case studies that advance the understanding and application of data mining techniques and knowledge discovery methodologies.
Objective: The primary objective of Data Mining and Knowledge Discovery is to promote research and innovation in data mining and knowledge discovery. The journal aims to explore algorithms, methodologies, and applications that enhance the extraction of useful patterns, knowledge, and insights from large datasets. By publishing high-quality research, the journal contributes to the advancement of data mining theory and its practical applications in various domains.
Interdisciplinary Focus: Data Mining and Knowledge Discovery adopts an interdisciplinary approach, welcoming contributions from various fields related to data mining and knowledge discovery, including but not limited to, Computer Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Data Science, Information Retrieval, Database Systems, Bioinformatics, Social Sciences. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced data mining techniques and solutions that address the complex challenges in diverse domains.
Global Reach and Impact: With a broad international readership and authorship, Data Mining and Knowledge Discovery has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in data mining worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as predictive analytics, clustering, classification, association rule mining, and anomaly detection.
High Standards and Rigorous Review: Maintaining high academic standards, Data Mining and Knowledge Discovery conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: Data Mining and Knowledge Discovery plays a significant role in advancing research and practice in data mining. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in data mining principles and practices. It serves as an essential resource for researchers, practitioners, and policymakers seeking to leverage data mining techniques to extract actionable insights, make informed decisions, and address real-world challenges in various domains.

Journal Home: Journal Homepage

Editor-in-Chief: Eyke HÃ¼llermeier

Scope: The Data Mining and Knowledge Discovery journal, published by Springer, is a leading peer-reviewed publication dedicated to the advancement of research in the areas of data mining, machine learning, and knowledge discovery. It serves as a platform for researchers, practitioners, and academics to exchange ideas and disseminate innovative findings in the field. Here is an overview of its key focus areas and scope:
1. Data Mining Algorithms and Techniques:
Research on the development and analysis of algorithms and techniques for data mining and knowledge discovery, including association rule mining, clustering, classification, regression, anomaly detection, and pattern recognition.
Advancements in optimization methods, ensemble learning techniques, deep learning approaches, and evolutionary algorithms for handling large-scale and complex datasets.
2. Knowledge Representation and Modeling:
Exploration of methods and frameworks for representing and modeling knowledge extracted from data, including symbolic representations, ontologies, semantic networks, and graph-based models.
Research on knowledge integration, fusion, and abstraction techniques for synthesizing information from multiple sources and domains.
3. Data Preprocessing and Cleaning:
Advancements in data preprocessing and cleaning techniques to improve the quality, consistency, and reliability of datasets for analysis.
Research on missing data imputation, noise removal, outlier detection, feature selection, and dimensionality reduction methods.
4. Text Mining and Natural Language Processing:
Exploration of text mining and natural language processing (NLP) techniques for extracting knowledge from unstructured textual data, including sentiment analysis, topic modeling, named entity recognition, and document clustering.
Research on information retrieval, text summarization, machine translation, and opinion mining from text data.
5. Big Data Analytics and Scalable Mining:
Research on scalable algorithms and systems for mining big data, including distributed computing frameworks, parallel processing techniques, and stream processing algorithms.
Advancements in techniques for mining temporal and spatial data, social media data, multimedia data, and heterogeneous data sources.
6. Knowledge Discovery in Complex Data:
Exploration of techniques for knowledge discovery in complex and heterogeneous data types, including time-series data, graph data, network data, and multimedia data.
Research on multistructured data mining, graph mining, network analysis, and relational data mining.
7. Applications of Data Mining and Knowledge Discovery:
Research on the application of data mining and knowledge discovery techniques in various domains, including healthcare informatics, finance, e-commerce, bioinformatics, social media analysis, cybersecurity, and environmental monitoring.
Studies on real-world case studies, applications, and success stories of data mining and knowledge discovery.
8. Privacy, Security, and Ethical Issues:
Exploration of privacy-preserving data mining techniques, secure data sharing mechanisms, and ethical considerations in data mining and knowledge discovery.
Research on the implications of data mining on individual privacy, data ownership, data governance, and regulatory compliance.
9. Interactive and Visual Data Mining:
Advancements in interactive and visual data mining techniques for exploratory data analysis, pattern discovery, and knowledge visualization.
Research on user-centered design, human-computer interaction, and visual analytics for supporting data-driven decision-making.
10. Emerging Trends and Future Directions:
Exploration of emerging trends, challenges, and opportunities in data mining and knowledge discovery research, including interdisciplinary collaborations, novel applications, and methodological advancements.
Research on the future evolution of data mining, including cognitive data mining, deep learning interpretability, explainable AI, and responsible data mining practices.