@inproceedings{f67c8d91e59840718cc36b1a8b7a44df,
title = "Using Data Mining Techniques to Identify Construction Claims Causes: A Case Study",
abstract = "The construction industry is a complex, multifaceted sector that is characterized by a high level of claims. Several studies have aimed to examine claims in the construction industry and the associated cases. However, the existing studies in this domain have been employed conventional statistical analysis. The research described in this paper exploited data mining techniques to accurately predict and rank the causes of construction claims. Data based on the studies of Zaneldin 2006 and 2018 studies were used to predict the ability of feature selection techniques to rank the causes of claims. Various feature selection techniques were applied, and the overlap in the ranked causes was compared with those identified in the previous studies.",
keywords = "Classification, Construction Claims, Data Mining, Feature Selection",
author = "\{Al Khaldi\}, Vasila and Nazar Zaki and Essam Zaneldin and Mohamed, \{Elfadil A.\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019 ; Conference date: 19-11-2019 Through 21-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICECTA48151.2019.8959800",
language = "English",
series = "2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2019",
address = "United States",
}