BASIC CONCEPT PYTHAGORAS TREE FOR CONSTRUCT DATA VISUALIZATION ON DECISION TREE LEARNing

DOI 10.5937/jaes17-21960

This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.

Volume 17 article 632 pages: 468 - 472

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.

1. F. Beck, M. Burch, T. Munz, L. Di Silvestro, and D. Weiskopf, “Generalized Pythagoras Trees for Visualizing Hierarchies,” in Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 2014, pp. 17–28.

2. A. E. Bosman, Het wondere onderzoekingsveld der vlakke meetkunde. Breda: N.V. Uitgeversmaatschappij Parcival, 1957.

3. B. Ratner, “Pythagoras: Everyone knows his famous theorem, but not who discovered it 1000 years before him,” J. Targeting, Meas. Anal. Mark., vol. 17, no. 3, pp. 229–242, Sep. 2009.

4. L. Teia, “Anatomy of the Pythagoras’ tree,” Aust. Sr. Math. J., vol. 30, no. 2, pp. 38–47, 2016.

5. L. T. Gomes, “Pythagoras Triples Explained via Central Squares,” Aust. Sr. Math. J., vol. 29, no. 1, pp. 7–15, 2015.

6. V. Dlab and K. S. Williams, “The Many Sides of the Pythagorean Theorem,” Coll. Math. J., vol. 50, no. 3, pp. 162–172, 2019.

7. J. R. Parada-Daza, M. I. Parada-Contzen, J. R. Parada-Daza, and M. I. Parada-Contzen, “Pythagoras and the Creation of Knowledge,” Open J. Philos., vol. 04, no. 01, pp. 68–74, Jan. 2014.

8. J. R. Quinlan, “Induction of Decision Trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.

9. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification And Regression Trees. Routledge, 2017.

10. J. R. Quinlan, C4.5: Programs for Machine Learning. 1993.

11. S. Swaminathan, “The Pythagorean Theorem,” J. Biodiversity, Bioprospecting Dev., vol. 01, no. 03, pp. 1–4, Sep. 2014.

12. M. F. Al-Saleh and A. E. Yousif, “Properties of the Standard Deviation that are Rarely Mentioned in Classrooms,” AUSTRIAN J. Stat., vol. 38, pp. 193–202, 2009.

13. E. W. Ambarsari, S. Khotijah, and L. Sunarmintyastuti, “Pemodelan Reward Rule Game Streamer Indonesia Tingkat Amatir dengan Orange Data Mining,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 4, no. 1, pp. 9–17, Aug. 2019.

14. W. A. C. Rojas and C. M. Villegas, “Graphical representation and exploratory visualization for decision trees in the KDD process,” Proc. - 2012 9th Electron. Robot. Automot. Mech. Conf. CERMA 2012, vol. 73, no. Dm, pp. 203–210, 2012.