Web-Based Application for Outliers Detection on Hotspot Data Using K-Means Algorithm and Shiny Framework

Workshop and International Seminar on Science of Complex Natural Systems

Suci, Agisha Mutiara Yoga Asmarani^1 ; Sitanggang, Imas Sukaesih^1

Computer Science Department, Faculty of Mathematics and Science, Bogor Agricultural University, Bogor

16680, Indonesia^1

关键词: Clustering results;    Fire prevention;    k-Means algorithm;    K-means clustering method;    Occurences;    Outliers detection;    Sum square error;    Web-based applications;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/31/1/012003/pdf
DOI  :  10.1088/1755-1315/31/1/012003

来源: IOP

【 摘 要 】

Outliers analysis on hotspot data as an indicator of fire occurences in Riau Province between 2001 and 2012 have been done, but it was less helpful in fire prevention efforts. This is because the results can only be used by certain people and can not be easily and quickly accessed by users. The purpose of this research is to create a web-based application to detect outliers on Hotspot data and to visualize the outliers based on the time and location. Outliers detection was done in the previous research using the k-means clustering method with global and collective outlier approach in Riau Province Hotspot data between 2001 and 2012. This work aims to develop a web-based application using the framework Shiny with the R programming language. This application provides several functions including summary and visualization of the selected data, clustering hotspot data using k-means algorithm, visualization of the clustering results and sum square error (SSE), and displaying global and collective outliers and visualization of outlier spread on Riau Province Map.