Application - Tree stream mining algorithm with Chernoff-bound

Guide

This application is the implementation of Tree stream mining algorithm with Chernoff-bound which is published in Tree stream mining algorithm with Chernoff-bound and standard deviation approach for big data stream, Journal of Big Data, volume 6, Article number: 58 (2019), ISSN: 2196-1115.

Base Framework

MOA (Massive Online Analysis) it the base framework that we used for ch.

• Massive Online Analysis - Home Page - Massive Online Analysis - Home Page.
• Massive Online Analysis - Github Page - Massive Online Analysis - Github Page

Datasets

MOA (Massive Online Analysis) it the base framework that we used for ch.

• Airline Dataset - Airline Dataset (9 GB).
• Traffic Dataset - Traffic Dataset (25 GB)

Application

Please download this application

• moa-ch.jar - Application jar file (12 MB).

Script to run LearnModel FIMT-DD-ch

java -cp moa-ch.jar moa.DoTask "LearnModelRegression -l trees.FIMTDD -s (ArffFileStream -f (traffic.arff)) -O (ch-traffic-demand.moa) -m 100000000"

Script to test EvaluateModel FIMT-DD-ch

java -cp moa-ch.jar moa.DoTask "EvaluateModelRegression -m file:ch-traffic-demand.moa -s (ArffFileStream -f (traffic.arff)) -i 100000"

Parameters in LearnModelRegression

-m : max instance
-s : stream file to learn by the model ( you can change this file as data training forthe model)
-O : save the model

Parameters in EvaluateModelRegression

-m : load model file
-s : stream file to learn by the model ( you can change this file to test the model)
-i : amount of instances to be tested