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A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach

by P. Anbalagan, R.M. Chandrasekaran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 2
Year of Publication: 2015
Authors: P. Anbalagan, R.M. Chandrasekaran
10.5120/ijca2015905346

P. Anbalagan, R.M. Chandrasekaran . A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach. International Journal of Computer Applications. 124, 2 ( August 2015), 5-9. DOI=10.5120/ijca2015905346

@article{ 10.5120/ijca2015905346,
author = { P. Anbalagan, R.M. Chandrasekaran },
title = { A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 2 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number2/22074-2015905346/ },
doi = { 10.5120/ijca2015905346 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:19.309173+05:30
%A P. Anbalagan
%A R.M. Chandrasekaran
%T A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 2
%P 5-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective and efficient strategies to acquire manage and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era, brings up challenges to traditional data mining algorithms. The processing capacity, architecture and algorithms of traditional database system are not coping with big data analysis. Big Data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this paper is to propose Parallel Weighted Decision Tree Classifier to handle complex spatial landslide big data using Map Reduce programming model. The Proposed Classifier performance is validated with massive dataset. The results indicate that our classifier exhibits both time efficiency and scalability.

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Index Terms

Computer Science
Information Sciences

Keywords

Big Data Classifier Spatial Data Map Reduce Landslide..

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