Title: A novel Cp-Tree-based co-located classifier for big data analysis

Authors: M. Venkatesan; T. Arunkumar; P. Prabhavathy

Addresses: School of Computing Science and Engineering, School of Information Technology and Engineering, VIT University, Vellore-632014, Tamilnadu, India ' School of Computing Science and Engineering, School of Information Technology and Engineering, VIT University, Vellore-632014, Tamilnadu, India ' School of Computing Science and Engineering, School of Information Technology and Engineering, VIT University, Vellore-632014, Tamilnadu, India

Abstract: 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 proposing novel co-located classifier to handle complex spatial landslide big data. Co-located classification primarily aims at predicting the class labels of the unknown data from the class co-located rules. The main focus is on building a co-located classifier which utilises Cp-Tree algorithm for co-located rule generation to analyse landslide data. The performance of proposed classifier is validated and compared with various data mining classifier.

Keywords: co-location; classification; Cp-Tree; rule; spatial data; landslide data; big data analysis; data mining.

DOI: 10.1504/IJCNDS.2015.070973

International Journal of Communication Networks and Distributed Systems, 2015 Vol.15 No.2/3, pp.191 - 211

Received: 02 Jun 2014
Accepted: 07 Feb 2015

Published online: 02 Aug 2015 *

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