Cite Score and Journal Rank of International Journal of Data Mining, Modelling and Management

About:

International Journal of Data Mining, Modelling and Management (IJDMMM) is a peer-reviewed academic journal that focuses on the research and development of methods and technologies for data mining, modeling, and management. The journal serves as a platform for the dissemination of innovative research that contributes to the extraction of meaningful insights from large datasets, the creation of accurate models, and the effective management of data in various domains.

Objective: The primary objective of the International Journal of Data Mining, Modelling and Management is to advance the field of data mining by publishing research that addresses theoretical, methodological, and practical aspects of data mining and its integration with modeling and data management. The journal covers a broad range of topics, including machine learning, statistical analysis, predictive modeling, big data analytics, data warehousing, and data governance. It aims to publish high-quality research that enhances the understanding and application of data mining techniques in solving complex problems.

Interdisciplinary Approach: The journal adopts an interdisciplinary approach by integrating research from fields such as computer science, statistics, mathematics, information systems, and business intelligence. This approach enables a comprehensive exploration of data mining, modeling, and management techniques, considering both the technical aspects and their applications in real-world scenarios. Contributions that explore the synergy between data mining, modeling, and data management across different industries and sectors are particularly encouraged.

Impact and Significance: The International Journal of Data Mining, Modelling and Management has a significant impact on both the academic and professional communities engaged in data science and analytics. The research published in the journal provides valuable insights into the latest developments in data mining, offering innovative solutions for data-driven decision-making and problem-solving. It serves as a key resource for researchers, data scientists, and practitioners who are involved in the extraction, modeling, and management of data in various fields such as finance, healthcare, marketing, and engineering.

Journal Home:  Journal Homepage

Editor-in-Chief:  Prof. John Wang

scope: The International Journal of Data Mining, Modelling and Management is a peer-reviewed academic journal dedicated to the exploration and dissemination of advanced methodologies and innovative applications in the fields of data mining, modeling, and management. The journal aims to provide a platform for researchers, practitioners, and academics to discuss the latest developments and challenges in these areas. Key topics of interest include:

1. Data Mining Techniques and Algorithms: Exploration of new and advanced data mining algorithms, including classification, clustering, association rule mining, and anomaly detection, tailored to various types of data.

2. Big Data Analytics: Research on methods and tools for analyzing large-scale datasets, focusing on scalability, efficiency, and the extraction of actionable insights from big data.

3. Predictive Modeling: Studies on the development and application of predictive models, including regression, time series forecasting, and machine learning models, to predict future trends and outcomes.

4. Data Preprocessing and Cleaning: Techniques and methodologies for preparing data for analysis, including data cleaning, normalization, transformation, and integration.

5. Data Management Strategies: Exploration of efficient data management techniques, including data warehousing, database management, and metadata management, to support data mining and analysis processes.

6. Text and Web Mining: Research on mining unstructured data, including text, web content, and social media data, to extract meaningful patterns and insights.

7. Data Visualization and Interpretation: Innovative approaches to data visualization that enhance the interpretation and communication of complex data mining results to various stakeholders.

8. Statistical Methods for Data Mining: Integration of statistical techniques with data mining approaches to improve the accuracy, reliability, and robustness of data-driven models.

9. Decision Support Systems: Development of data mining and modeling techniques that enhance decision support systems (DSS) across various industries and applications.

10. Knowledge Discovery in Databases (KDD): Studies focusing on the end-to-end process of knowledge discovery, from data selection and preprocessing to interpretation and application of the discovered knowledge.

Print ISSN:  1759-1163

Electronic ISSN:  1759-1171

Abstracting and Indexing:  Scopus

Imapct Factor 2023:  0.4

Subject Area and Category:  Business, Management and Accounting,Management Information Systems,Computer Science,Computer Science Applications,Mathematics,Modeling and Simulation

Publication Frequency:  

H Index:  16

Best Quartile:

Q1:  

Q2:  

Q3:  

Q4:  Computer Science Applications

Cite Score:  1.1

SNIP:  0.216

Journal Rank(SJR):  0.17

Latest Articles:   Latest Articles in International Journal of Data Mining, Modelling and Management

Guidelines for Authors: International Journal of Data Mining, Modelling and Management Author Guidelines

Paper Submissions: Paper Submissions in International Journal of Data Mining, Modelling and Management

Publisher:  Inderscience Publishers

Country:  Switzerland