Apriori Algorithm for Frequent Pattern Mining for Public Librariesin United States

Muhammad Muhajir, Ayundyah Kesumawati, Satibi Mulyadi

2018

Abstract

This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job and also give the descriptive by map representation about the number of public library in United States. In addition, we demonstrate the use of Big ML tool for association rule mining using Apriori algorithm. There are three attributes of public libraries is used among other City, Location type, and State. The result shows that there are six most recommended rules with confidence value ≥ 0.8 that are, Rules {City includes Chicago} => {Location type=Branch library}; Rules {City includes Brooklyn} => {Location type=Branch library}; Rules {City includes Losangeles} => {Location type=Branch library}; Rules {City includes Baltiomore} => {Location type=Branch library}; Rules {City includes Sandiego} => {Location type=Branch library} and Rules {City includes Miami} => {Location type=Branch library}. The six rules means that the most visited public libraries by population of legal service area over one million people is a branch library.

Paper Citation

in Harvard Style

Muhajir M., Kesumawati A. and Mulyadi S. (2018). Apriori Algorithm for Frequent Pattern Mining for Public Librariesin United States.In Proceedings of the International Conference on Mathematics and Islam - Volume 1: ICMIs, ISBN 978-989-758-407-7, pages 60-64. DOI: 10.5220/0008517200600064

in Bibtex Style

@conference{icmis18,
author={Muhammad Muhajir and Ayundyah Kesumawati and Satibi Mulyadi},
title={Apriori Algorithm for Frequent Pattern Mining for Public Librariesin United States},
booktitle={Proceedings of the International Conference on Mathematics and Islam - Volume 1: ICMIs,},
year={2018},
pages={60-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008517200600064},
isbn={978-989-758-407-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Mathematics and Islam - Volume 1: ICMIs,
TI - Apriori Algorithm for Frequent Pattern Mining for Public Librariesin United States
SN - 978-989-758-407-7
AU - Muhajir M.
AU - Kesumawati A.
AU - Mulyadi S.
PY - 2018
SP - 60
EP - 64
DO - 10.5220/0008517200600064