International Journal of Data Mining, Modelling and Management

Aims & Scope

Facilitating transformation from data to information to knowledge is paramount for organisations.

Companies are flooded with data and conflicting information, but with limited real usable knowledge.

However, rarely should a process be looked at from limited angles or in parts.

Isolated islands of data mining, modelling and management (DMMM) should be connected.

IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications.

Topics covered include: -Artificial intelligence- Biomedical science- Business analytics/intelligence, process modelling- Computer science, database management systems- Data management, mining, modelling, warehousing- Engineering- Environmental science, environment (ecoinformatics)- Information systems/technology, telecommunications/networking- Management science, operations research, mathematics/statistics- Social sciences- Business/economics, (computational) finance- Healthcare, medicine, pharmaceuticals- (Computational) chemistry, biology (bioinformatics)- Sustainable mobility systems, intelligent transportation systems- National security

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	0.5
2024	0.40

Journal Rank

Year	Value
2024	23710

Journal Citation Indicator

Year	Value
2024	45

SJR (SCImago Journal Rank)

Year	Value
2024	0.161

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	17

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting, Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Hybrid classifier model for big data by leveraging map reduce framework

  Citation: 41

  Authors: V., K. Rajendra, K. Sudheer, A.V. Krishna, M. Venkat

• Is an ordinal class structure useful in classifier learning?

  Citation: 26

  Authors: Jens C., Eyke

• A Review on Breast Cancer Detection using Machine Learning Techniques

  Citation: 22

  Authors: Sowjanya, Sudheer Reddy

• Using trajectories for collaborative filtering-based POI recommendation

  Citation: 20

  Authors: Haosheng, Georg

• Privacy preserving record linkage approaches

  Citation: 17

  Authors: Vassilios S., Alexandros, Vassilios K.

• Mining event histories: a social science perspective

  Citation: 14

  Authors: Gilbert, Alexis, Nicolas S., Matthias

• A framework for Arabic sentiment analysis using supervised classification

  Citation: 14

  Authors: Rehab M., Islam

• Discovering (frequent) constant conditional functional dependencies

  Citation: 12

  Authors: Thierno, Nöel, Jean Marc

• Combining multiple classifiers for wrapper feature selection

  Citation: 12

  Authors: Kyriacos, Sherry Y., Xiaohui

• A relational perspective on spatial data mining

  Citation: 12

  Authors: Donato

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