International Journal of Data Mining Modelling and Management

Scope & Guideline

Navigating the Evolving Landscape of Data Mining

Introduction

Explore the comprehensive scope of International Journal of Data Mining Modelling and Management through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore International Journal of Data Mining Modelling and Management in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1759-1163
PublisherINDERSCIENCE ENTERPRISES LTD
Support Open AccessNo
CountrySwitzerland
TypeJournal
Convergefrom 2008 to 2024
AbbreviationINT J DATA MIN MODEL / Int. J. Data Min. Model. Manag.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressWORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND

Aims and Scopes

The International Journal of Data Mining Modelling and Management focuses on advancing the fields of data mining, modeling, and management through innovative research methodologies and applications across diverse domains.
  1. Data Mining Techniques:
    The journal emphasizes various data mining techniques, including frequent pattern mining, clustering, and classification, aimed at extracting meaningful insights from large datasets.
  2. Machine Learning Applications:
    It covers a wide range of machine learning algorithms and their applications in different fields, such as healthcare, finance, and social media, showcasing the integration of machine learning with data mining.
  3. Big Data Management:
    The journal explores big data paradigms and solutions, including data pipeline development, data quality optimization, and data management frameworks, to effectively handle the complexities of large-scale data.
  4. Interdisciplinary Research:
    Research that bridges multiple disciplines, such as combining data analytics with social sciences, economics, and environmental studies, is a key focus area, promoting a holistic approach to data-driven decision-making.
  5. Emerging Technologies:
    The journal also emphasizes the impact of emerging technologies like deep learning, IoT, and natural language processing in advancing data mining and modeling methodologies.
The journal has identified several trending and emerging themes that reflect the evolving landscape of data mining and modeling, highlighting areas of increasing research interest.
  1. Deep Learning Innovations:
    There is a significant increase in publications focusing on deep learning techniques, particularly in applications related to image processing, natural language processing, and complex data analysis.
  2. IoT and Cybersecurity:
    Research exploring the intersection of IoT technologies and cybersecurity, including intrusion detection systems and data protection methodologies, has gained traction in response to rising security concerns.
  3. Sentiment Analysis and Social Media Mining:
    The journal has seen a rise in studies analyzing social media data for sentiment analysis, reflecting the growing importance of understanding public opinion and behavior through data.
  4. Healthcare Applications:
    There is an emerging trend in utilizing data mining and machine learning for healthcare applications, such as disease prediction and patient management, driven by the need for data-driven healthcare solutions.
  5. Data Ethics and Governance:
    Increasing attention is being paid to issues of data ethics, privacy, and governance, highlighting the need for responsible data management practices in research and application.

Declining or Waning

While the journal covers a broad spectrum of topics, some themes have shown a decline in publication frequency, indicating a potential waning interest or saturation in these areas.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in studies focusing solely on traditional statistical methods, as the field shifts towards more innovative and computationally intensive approaches like machine learning.
  2. Basic Data Visualisation Techniques:
    Publications that address basic data visualization techniques are less frequent, suggesting a move towards more complex and interactive visualization methods that integrate advanced analytics.
  3. Niche Applications:
    Research on niche applications of data mining, such as specific case studies in localized contexts, seems to be declining, possibly due to a preference for broader, more generalizable findings.
  4. Static Data Analysis:
    There is a waning focus on static data analysis techniques, as real-time data processing and analysis gain prominence, reflecting the industry's shift toward dynamic data environments.
  5. Simple Predictive Models:
    The journal has seen fewer contributions centered around simple predictive models, as researchers increasingly explore hybrid and ensemble methods that promise greater accuracy and robustness.

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