Metrika članka

  • citati u SCindeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[=>]
  • posete u poslednjih 30 dana:7
  • preuzimanja u poslednjih 30 dana:5
članak: 6 od 25  
Back povratak na rezultate
Journal of Applied Engineering Science
2019, vol. 17, br. 4, str. 468-472
jezik rada: engleski
vrsta rada: izvorni naučni članak
objavljeno: 20/12/2019
doi: 10.5937/jaes17-21960
Creative Commons License 4.0
Basic concept Pythagoras tree for construct data visualization on decision tree learning
(naslov ne postoji na srpskom)
aUniversitas Indraprasta PGRI, Engineering and Computer Science Faculty, Indonesia
bPost Graduate of Universitas Indraprasta PGRI, Indonesia
cSekolah Tinggi Ilmu Manajemen Sukma, Indonesia



(ne postoji na srpskom)
Decision Tree in Data Mining frequently used to learn the pattern by interpreting data. A hierarchy of tree model in Decision Tree as data visualization which often used makes fully load space. Another option in using model is Phytagoras Tree. Pythagoras Tree in this study is the basic concept of Pythagorean Theorem that used by a binary hierarchy with a fractal technique which the shape using the square as branches enclose a right triangle. A fractal of Pythagoras Tree is the dataset which split the subsets into trunks and leaves. Construct a fractal of Pythagoras Tree depends on the angle θ for build branches followed by square area. Pythagoras Tree model is an easy way to understanding the dataset based on the size of the square. The smaller the size, the fewer instances in the rectangle. Also, data associations easily traced when filled with color.

Ključne reči


Al-Saleh, M.F., Yousif, A.E. (2009) Properties of the standard deviation that are rarely mentioned in classrooms. Austrian Journal of Statistics, 38(3), 193-202
Ambarsari, E.W., Khotijah, S., Sunarmintyastuti, L. (2019) Pemodelan reward rule game streamer Indonesia Tingkat amatir dengan orange data mining. String (Satuan Tulisan Riset Dan Inovasi Teknologi), 4(1), 9-17
Beck, F., Burch, M., Munz, T., di Silvestro, L., Weiskopf, D. (2014) Generalized Pythagoras trees for visualizing hierarchies. u: 5th International Conference on Information Visualization Theory and Applications, Proceedings, 17-28
Bosman, A.E. (1957) Het wondere onderzoekingsveld der vlakke meetkunde. Breda: N.V. Uitgeversmaatschappij Parcival
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (2017) Classification and regression trees. Routledge
Dlab, V., Williams, K.S. (2019) The many sides of the Pythagorean Theorem. College Mathematics Journal, 50(3), 162-172
Gomes, L.T. (2015) Pythagoras triples explained via central squares. Aust. Sr. Math. J, 29(1), 7-15
Parada-Daza, J.R., Parada-Contzen, M.I. (2014) Pythagoras and the creation of knowledge. Open Journal of Philosophy, 04(01), 68-74
Quinlan, J.R. (1993) C4.5: Programs for machine learning
Quinlan, J.R. (1986) Induction of decision trees. Machine Learning, 1(1), 81-106
Ratner, B. (2009) Pythagoras: Everyone knows his famous theorem, but not who discovered it 1000 years before him. Journal of Targeting, Measurement and Analysis for Marketing, 17(3), 229-242
Rojas, W.A.C., Villegas, C.M. (2012) Graphical representation and exploratory visualization for decision trees in the KDD process. u: 2012 9th Electron. Robot. Automot. Mech. Conf. CERMA 2012, Proc, vol. 73, no. Dm, pp. 203-210
Swaminathan, S. (2014) The Pythagorean Theorem. Journal of Biodiversity, Bioprospecting and Development, 01(03), 1-4
Teia, L. (2016) Anatomy of the Pythagoras' tree. Aust. Sr. Math. J, 30(2), 38-47