Aims and Scope

DataXplore - Advancing Data Mining for Future Discoveries is dedicated to being a leading platform where researchers and practitioners can actively contribute, collaborate, and innovate in the dynamic field of data mining. Our primary goal is to publish high-quality research papers, review articles, and case studies that explore cutting-edge approaches and novel applications across various domains. As an integral part of the data mining community, we foster interdisciplinary research among experts in data mining, statistics, machine learning, artificial intelligence, and related fields. Our journal aims to promote the dissemination of knowledge and best practices, encouraging the widespread adoption of advanced data mining techniques in research, industry, and society. Moreover, we are committed to addressing the challenges and ethical considerations that arise in data mining, ensuring a responsible and transparent use of data for making significant discoveries that will shape the future positively. Beyond our core mission, we also aspire to inspire and support young researchers and students, fostering talent and expertise to cultivate the next generation of data mining experts. By embracing this comprehensive approach, DataXplore endeavors to be the go-to platform for all those invested in the advancement and future discoveries enabled by data mining.

The scope of the journal includes, but is not limited to, the following areas of research:

• Big Data Analytics: Novel approaches to handle and analyze massive and complex datasets.
• Machine Learning and Deep Learning: Applications of advanced learning algorithms in data mining.
• Pattern Recognition and Data Classification: Techniques for identifying patterns and classifying data.
• Data Preprocessing and Cleaning: Methods to enhance data quality and remove noise.
• Clustering and Unsupervised Learning: Approaches for discovering hidden structures in data.
• Association Rule Mining: Identification of interesting associations between data items.
• Sequential Pattern Mining: Uncovering patterns in sequential data.
• Graph Mining: Techniques for analyzing and extracting knowledge from graph-structured data.
• Text and Sentiment Analysis: Methods for mining text data and sentiment analysis.
• Web Mining and Social Media Analysis: Extracting insights from web and social media data.
• Time Series Data Mining: Analyzing temporal data and predicting future trends.
• Privacy-Preserving Data Mining: Techniques to protect individual privacy while mining data.
• Data Mining for Healthcare and Biomedicine: Applications in medical diagnosis, genomics, and health analytics.
• Data Mining for Business and Marketing: Leveraging data mining in business intelligence and marketing strategies.
• Ethical and Legal Issues in Data Mining: Discussions on responsible data mining practices and privacy regulations.

The journal aims to be inclusive and open to innovative data mining research in various domains, promoting the exchange of knowledge and ideas to drive advancements and facilitate future discoveries. Authors are encouraged to present research that contributes to the ever-growing field of data mining and its potential impact on various sectors, driving progress and benefiting society as a whole.

Bibliographic Information

Print ISSN: pending
Electronic ISSN: pending
DOI: 10.5281/zenodo

Registered with the National Library of Mauritius

Archival Content

Content published are archived on zenodo which is funded by CERN, OpenAIRE and the European Commission.

Open Access

DataXplore - Advancing Data Mining for Future Discoveries is an open access journal. All articles are immediately available to read and reuse upon publication. More information about our Open Access policy can be found on our open access page and copyright page.