Designing Customer Target Recommendation System Using K-Means Clustering Method
Evasaria M. Sipayung(1*), Herastia Maharani(2), Benny A. Paskhadira(3)
(1) Departemen Sistem Informasi Institut Teknologi Harapan Bangsa
(2) Departemen Sistem Informasi Institut Teknologi Harapan Bangsa
(3) Departemen Sistem Informasi Institut Teknologi Harapan Bangsa
(*) Corresponding Author
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[1] Sipayung E.M., Fiarni Cut, Tanudjaya R., “Decision Support System for Potential Sales Area of Product Marketing using Classification and Clustering Methods”, Proceeding International Seminar on Industrial Engineering and Management, 2015, pp. 33-39.
[2] Sipayung E.M., Fiarni Cut, Tanudjaya R., “Modeling Data Mining Dynamic Code Attributes with Scheme Definition Technique”, Proceeding Electrical Engineering, Computer Science and Informatics, 2014, pp. 25-28.
[3] Larosse, Daniel T., Discovering Knowledge in Data: An Introduction to Data Mining, Wiley-Interscience, 2005.
[4] Kusrini and Lutfi E.T., Algoritma Data Mining, Yogyakarta, Indonesia: Andi, 2009.
[5] Pavel, Berkhin, Survey on Clustering Data Mining Techniques. San Jose, CA: Accrue Software, 2002.
[6] M. Kaur and U. Kaur, “Comparison Between K-Means and Hierarchical Algorithm Using Query Reduction,” International Journal of Advance Research in Computer Science and Software Engineering, Vol. 3, No. 7, pp. 1454-1459, 2013.
[7] E. Turban, J. E. Aronson, and L. P. Ting, Decision Support System and Intelligent System, New Jersey: Pearson Education, Inc, 2005.
[8] Davies, David L., Donald W. Bouldin, “Cluster Separation Measure”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 95-104, 2013.
DOI: https://doi.org/10.22146/ijitee.25155
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