@article{scholars4519,
            note = {cited By 5; Conference of 1st International Conference on Advanced Data and Information Engineering, DaEng 2013 ; Conference Date: 16 December 2013 Through 18 December 2013; Conference Code:102583},
          volume = {285 LN},
             doi = {10.1007/978-981-4585-18-7{$_7$}{$_1$}},
         address = {Kuala Lumpur},
           title = {Development of Web services fuzzy quality models using data clustering approach},
            year = {2014},
       publisher = {Springer Verlag},
         journal = {Lecture Notes in Electrical Engineering},
           pages = {631--640},
            isbn = {9789814585170},
          author = {Hasan, M. H. and Jaafar, J. and Hassan, M. F.},
            issn = {18761100},
        abstract = {This paper presents the fuzzy clustering of web services' quality of service (QoS) data using Fuzzy C-Means (FCM) algorithm. It was conducted based on actual QoS data gathered from the network. The work involved three data sets that represented three different QoS parameters. Each data set contained 1500 data points. The clustering was validated using Xie-Beni index to ensure that it performed optimally. As a result, three fuzzy quality models were produced that represented the three QoS parameters. The work implies potential new findings on fuzzy-based web services' applications, mainly in reducing computational complexity. The work also benefits the less technical-knowledgeable requestors as the fuzzy quality models can guide them to find services with realistic QoS performance. For future work, the fuzzy quality models will be employed in web services' QoS monitoring application. They will also be equipped with an adaptive mechanism that supports the dynamic nature of web services. {\^A}{\copyright} Springer Science+Business Media Singapore 2014.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958525875&doi=10.1007\%2f978-981-4585-18-7\%5f71&partnerID=40&md5=bc4935cef15a33c6984490f16bb75966},
        keywords = {Clustering algorithms; Fuzzy clustering; Fuzzy systems; Web services; Websites, Adaptive mechanism; Clustering; Data clustering; Fuzzy C mean; Fuzzy C-means algorithms; Fuzzy qualities; QoS monitoring; Qos performance, Quality of service}
}