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International Journal of Business Intelligence and Data Mining is a peer-reviewed journal published by Inderscience, covering Data Mining Algorithms and Applications, Data Management and Algorithms, Text and Document Classification Technologies.
| Publisher | Inderscience |
|---|---|
| Reference style | Author–year (Harvard/AGSM) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', International Journal of Business Intelligence and Data Mining, 12(3), pp. 45–58.
Formats any DOI in International Journal of Business Intelligence and Data Mining style. No sign-up. |
| Publishes research in | Data Mining Algorithms and Applications Data Management and Algorithms Text and Document Classification Technologies Educational Technology and Assessment Recommender Systems and Techniques |
| ISSN | 1743-8187 |
| Citation impact (2-yr) | 0.24 |
| h-index | 27 |
| i10-index | 99 |
| Total citations | 3,843 |
| Top institutions publishing here | Anna University, Chennai |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
In an urban area, the demand for taxis is not always matched up with the supply. This paper proposes mining historical data to predict demand distributions with respect to contexts of time, weather, and taxi location. The four-step process consists of data filtering, clustering, semantic annotation, and hotness calculation. The results of three clustering algorithms…
Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response…
Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data. Fortunately, Missing Data Imputation Techniques (MDITs) can be used to improve data quality.…
To discover hidden correlations, association rule mining methods use two important constraints known as support and confidence. However, mining methods are often unable to find the best value for these constraints: large number of rules when these thresholds are low; very few rules when these thresholds are high. In addition, regardless of these above thresholds,…
Urban real estate property values are mainly conditioned by several aspects, which can be summarised in two main classes: intrinsic and extrinsic ones. Intrinsic characters are specific goods while extrinsic features are related to a diversity of goods. Therefore, there is an extremely close correlation between ‘rigidity location’ of property (fixed location) and its value.…
