Abstract
Up to this moment, association rules mining are one of the most important issues in data mining application. One of the commonly and popular techniques used in data mining application is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, so called SLP-Growth (Significant Least Pattern Growth) proposed by [9] for capturing interesting rules in student suffering mathematics anxiety dataset. The dataset was taken from a survey on exploring mathematics anxiety among engineering students in Universiti Malaysia Pahang (UMP). The results of this research will provide useful information for educators to make a decision on their students more accurately, and to adapt their teaching strategies accordingly. It also can be helpful to assist students in handling their fear of mathematics and useful in increasing the quality of learning.
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Herawan, T., Vitasari, P., Abdullah, Z. (2011). Mining Interesting Association Rules of Student Suffering Mathematics Anxiety. In: Zain, J.M., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22191-0_43
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