International Journal of Business Intelligence and Data Mining

Aims & Scope

IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining.

Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling tasks.

IJBIDM highlights intelligent techniques used for business modelling, including all areas of data visualisation, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, data mining techniques, tools and applications, neurocomputing, evolutionary computing, fuzzy techniques, expert systems, knowledge filtering, and post-processing.

Topics covered include Data extraction/reporting/cleaning/pre-processing OLAP, decision analysis, causal modelling Reasoning under uncertainty, noise in data Business intelligence cycle Model specification/selection/estimation Web technology, mining, agents Fuzzy, neural, evolutionary approaches Genetic algorithms, machine learning, expert/hybrid systems Bayesian inference, bootstrap, randomisation Exploratory/automated data analysis Knowledge-based analysis, statistical pattern recognition Data mining algorithms/processes Classification, projection, regression, optimisation clustering Information extraction/retrieval, human-computer interaction Multivariate data visualisation, tools.

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	24556

Journal Citation Indicator

Year	Value
2024	75

SJR (SCImago Journal Rank)

Year	Value
2024	0.150

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	24

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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

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting and Decision Sciences, 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.

• Context-aware taxi demand hotspots prediction

  Citation: 150

  Authors: Han wen, Yu chin, Jane Yung jen

• Support vector machines based on K-means clustering for real-time business intelligence systems

  Citation: 80

  Authors: Jiaqi, Xindong, Chengqi

• Missing Data Imputation Techniques

  Citation: 42

  Authors: Qinbao, Martin

• Fuzzy clustering of intuitionistic fuzzy data

  Citation: 41

  Authors: Nikos, Dimitris K., Evangelos E., Ioannis

• Fragmenting very large XML data warehouses via K-means clustering algorithm

  Citation: 40

  Authors: Alfredo, Jerome, Hadj

• Bare ownership evaluation. Hedonic price model vs. artificial neural network

  Citation: 37

  Authors: Pierluigi, Francesco

• Redundant association rules reduction techniques

  Citation: 37

  Authors: Mafruz Zaman, David, Kate

• Using geographically weighted regression for housing market segmentation

  Citation: 33

  Authors: Benedetto, Piergiuseppe, Antonello, Beniamino

• Bankruptcy prediction for Japanese firms: using Multiple Criteria Linear Programming data mining approach

  Citation: 31

  Authors: Wikil, Yong, Susan W., Gang

• A hybrid framework for job scheduling on cloud using firefly and BAT algorithm

  Citation: 31

  Authors: Bhagavathi, Dassan Paul

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