Forthcoming articles


International Journal of Knowledge Engineering and Data Mining


These articles have been peer-reviewed and accepted for publication in IJKEDM, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.


Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.


Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.


Articles marked with this Open Access icon are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.


Register for our alerting service, which notifies you by email when new issues of IJKEDM are published online.


We also offer RSS feeds which provide timely updates of tables of contents, newly published articles and calls for papers.


International Journal of Knowledge Engineering and Data Mining (2 papers in press)


Regular Issues


  • Application and Comparison of Neural Network, C5.0, and Classification and Regression Trees (CART) algorithms in the Credit Risk Evaluation Problem (Case Study: A Standard German Credit Dataset)   Order a copy of this article
    by Mahdi Massahi Khoraskani,, Fahimeh Kheradmand, Alireza Arshadi Khamseh 
    Abstract: Due to the reducing global economic stability, the demand of banks for predicting their customer's credit risk has significantly increased and has become more critical, still challenging than ever. This paper addresses the problem of credit risk evaluation of banks customers utilizing data mining tools. Three classification techniques include: Neural Network, C5.0, and Classification and Regression Trees (CART) algorithms. In order to evaluate the performance of the classification techniques, an innovative two-stage evaluation process is proposed. firstly, the optimal status of algorithms is found by tuning its parameters. secondly, these tuned algorithms are ranked by the Analytical Hierarchy Process (AHP) method while four criteria of overall accuracy, precision, sensitivity, and specificity are considered. As a case study, a standard German credit dataset are used to validate the performance of the proposed algorithms. It is illustrated that the Neural Network algorithm is the superior algorithm to evaluate bank customers' credit risk.
    Keywords: credit risk evaluation; data mining; classification; neural networks; C5.0; classification and regression trees; CARTs; analytical hierarchy process; AHP.
    DOI: 10.1504/IJKEDM.2017.10006181
  • Rare Association Rule Mining: A Systematic Review   Order a copy of this article
    by Anindita Borah, Bhabesh Nath 
    Abstract: One of the indispensable tasks of data mining is the extraction of significant and meaningful association rules. Whereas the extraction of frequent patterns using association rule mining is an imperative field of research, the idea of generating patterns that do not appear frequently in a database has grabbed the attention of researchers in recent years. The infrequent items or more commonly known as the rare items represent unknown or unpredictable associations and are therefore more interesting than the frequent ones. This study aims to provide a broad systematic review of the area of rare association rule mining. In this paper, a methodical analysis of the rare itemset and rare rule generation techniques in static and dynamic environment is presented. This paper also attempts to feature the current status and future perspectives of rare association rule mining along with some major research challenges.
    Keywords: Association rule; Rare itemset; Rare association rule; Rare pattern; Systematic review.
    DOI: 10.1504/IJKEDM.2017.10007846