Vol.7, No.4, November 2018.                                                                                                                                                                             ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Association Rule Mining for Improvement of IT Project Management


Snezhana Sulova


© 2018 Snezhana Sulova, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 7, Issue 4, Pages 717-722, ISSN 2217-8309, DOI: 10.18421/TEM74-03, November 2018.


Received: 18 September 2018.
Accepted: 03 November 2018.
Published: 26 November 2018.




In this research we extract knowledge from human resources data, accumulated in IT companies for the right selection of teams to work on software projects. We are looking for interesting and unknown dependencies and connections in the data, based on which managers can form more cohesive and professionally working project teams. The proposed approach to improve the selection of teams working on IT projects is based on association rule mining and can be used by IT managers to select the members of the teams. The approbation of the proposed approach is made using the software product RapidMiner.


Keywords –IT project, Project team, Association Rule Mining, Apriori, FP-Growth.



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