Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Scope & Guideline

Pioneering innovative methodologies for a data-driven world.

Introduction

Welcome to the Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1942-4787
PublisherWILEY PERIODICALS, INC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2011 to 2024
AbbreviationWIRES DATA MIN KNOWL / Wiley Interdiscip. Rev.-Data Mining Knowl. Discov.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104

Aims and Scopes

The journal 'Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery' focuses on the integration of data mining techniques across various domains, emphasizing knowledge discovery processes and interdisciplinary applications. The journal aims to provide a platform for researchers to present innovative methodologies, reviews, and applications that contribute to advancing the field of data mining and its intersections with other disciplines.
  1. Interdisciplinary Applications of Data Mining:
    The journal emphasizes the application of data mining techniques across diverse fields such as healthcare, finance, and social sciences, showcasing how these methods can provide insights and solutions tailored to specific domains.
  2. Innovative Methodologies and Techniques:
    A core focus is on advancing and reviewing new methodologies in data mining, including machine learning, deep learning, and artificial intelligence, highlighting their effectiveness and potential impacts.
  3. Knowledge Discovery Processes:
    The journal explores the process of knowledge discovery from data, including methods for data preprocessing, pattern recognition, and the interpretation of results, contributing to better understanding and utilization of data.
  4. Ethical and Privacy Considerations:
    There is a consistent focus on the ethical implications and privacy concerns associated with data mining practices, especially in sensitive areas such as healthcare and personal data.
  5. Emerging Technologies:
    The journal is dedicated to exploring emerging technologies related to data mining, such as blockchain, digital twins, and quantum computing, reflecting the evolving landscape of data science.
Recent publications in the journal indicate a shift towards several trending and emerging themes that reflect the current state of research and technological advancements in data mining and knowledge discovery.
  1. Integration of AI and Machine Learning:
    There is a growing emphasis on the integration of artificial intelligence and machine learning techniques in data mining applications, demonstrating their effectiveness in various fields such as healthcare, finance, and environmental science.
  2. Healthcare Applications:
    The trend of applying data mining techniques to healthcare continues to rise, with numerous studies focusing on improving patient outcomes, predictive modeling for diseases, and the use of digital health technologies.
  3. Explainable AI (XAI):
    The relevance of explainable AI is increasingly recognized, with a focus on developing methodologies that enhance the interpretability of machine learning models, especially in critical fields like healthcare and finance.
  4. Ethical AI and Privacy Preservation:
    Emerging themes around ethical AI practices and privacy-preserving data mining techniques are gaining traction, reflecting the growing concern over data privacy and ethical implications in data science.
  5. Interdisciplinary Research:
    There is a noticeable trend towards interdisciplinary research that combines data mining with fields such as social sciences, environmental studies, and engineering, highlighting the versatile applications of data mining methodologies.

Declining or Waning

As the field of data mining evolves, certain themes have shown a decline in prominence within the recent publications of the journal. This reflects shifting interests and advancements in technology and methodologies.
  1. Traditional Statistical Methods:
    There has been a notable decrease in the focus on conventional statistical techniques for data analysis, as newer, more sophisticated machine learning and AI methodologies gain traction.
  2. Basic Data Mining Concepts:
    Basic concepts of data mining, such as simple classification and clustering techniques, appear less frequently as the field moves towards more complex and integrated approaches.
  3. Single-Domain Studies:
    Research that focuses exclusively on single-domain applications without interdisciplinary perspectives is becoming less common, as the journal emphasizes cross-domain applications and methodologies.
  4. Descriptive Analytics:
    There is a waning interest in purely descriptive analytics, with a shift towards predictive and prescriptive analytics that leverage advanced algorithms and machine learning techniques.

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