Data Mining Integration with PostgreSQL Extension by K-Means, ID3 and 1R Method

Tri Wahyuningsih, I Ketut Gunawan, Abdullah Dwi Srenggini, Henry Riyandi

Abstract


Data mining is a tool that allows users to quickly access large amounts of data. The purpose of this study was to analyze the integration of data mining technique algorithms into the PostgreSQL database management system. The method used in this research is K-Means, ID3 and 1R, the tools used to implement data mining using RapidMiner and PostgreSQL tools. In this study, the number of rows to be analyzed is 100,000 records, 500,000 records, and 1,000,000 records. The results obtained are the algorithm implemented to validate the data by using an experimental design that serves to observe the time that the analysis of the algorithm that has been integrated into the DBMS is smaller than the results from Rapidminer. As the number of records increases, data analysis becomes difficult using RapidMiner.Data mining techniques, Database management system, Partition, Response time


Article Metrics

Abstract: 851 Viewers PDF: 375 Viewers

Keywords


Data Mining Techniques; Database Management System; Partition; Response time

Full Text:

PDF


References


Maryanto, B. (2017). Big Data dan Pemanfaatannya dalam Berbagai Sektor. Media Informatika, Vol. 16 No. 2.

Krishna, S. 1992, “Introduction to database and knowledge-base systems”, Jakarta, World Scientific

Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.

Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017).

Boehm, B., Abts, C. y Chulani, S., “Software development cost estimation approaches-a survey”, Annals of Software Engineering 10, 2000, pp. 177-205

Wieczorek, I. y Briand, L., Resource estimation in software engineering, Technical Report, International Software Engineering Research Network, 2001.

Piotrowski, A.P., 2017. Review of Differential Evolution population size. Swarm Evol. Comput. 32, 1–24. https://doi.org/10.1016/j.swevo.2016.05.003

Kaya, I., 2009. A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny). 179, 1552– 1566. https://doi.org/10.1016/j.ins.2008.09.024

Chauhan, Sonam S, Deskmukh P R., Literature Review on Information Extraction by Partitioning. International Journal of Computer Science and Mobile Computing. Vol 2. 2013.

Gaitán-Angulo M, Jairo Enrique Santander Abril, Amelec Viloria, Julio Mojica Herazo, Pedro Hernández Malpica, Jairo Luis Martínez Ventura, Lissette Hernández-Fernández. (2018) Company Family, Innovation and Colombian Graphic Industry: A Bayesian Estimation of a Logistical Model. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.

Lubis, J. H. ( 2017 ). Analisa Performansi Query pada Database Smell. Jurnal Mantik Penusa, ISSN:2088-3943.


Refbacks

  • There are currently no refbacks.



Barcode

IJIIS: International Journal of Informatics and Information Systems

ISSN:2579-7069 (Online)
Organized by:Departement of Information System, Universitas Amikom Purwokerto, IndonesiaFaculty of Computing and Information Science, Ain Shams University, Cairo, Egypt
Website:www.ijiis.org
Email:husniteja@uinjkt.ac.id (publication issues)
  taqwa@amikompurwokerto.ac.id (managing editor)
  contact@ijiis.org (technical & paper handling issues)

 This work is licensed under a Creative Commons Attribution-ShareAlike 4.0