Title: Association mining of dependency between time series using Genetic Algorithm and discretisation

Authors: Mourad Ykhlef

Addresses: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia

Abstract: Association rule mining is one of the most popular data-mining techniques used to find associations existing between a set of objects or data. A time series is a sequence of observations stamped over the time; Time-series analysis has been used in a variety of applications like: business and health. The application of association mining to time series is very promising. The purpose of this article is to propose a new fast algorithm to discover the association that can exist between two time series. We use discretisation to segment time series to a number of shapes, and we classify these shapes to pre-defined shape classes to generate association rules using Genetic Algorithm (GA).

Keywords: time series; discretisation; GAs; genetic algorithms; association mining; dependency rules; association rules; data mining; shape classification.

DOI: 10.1504/IJBIDM.2011.038274

International Journal of Business Intelligence and Data Mining, 2011 Vol.6 No.1, pp.55 - 70

Published online: 22 Apr 2015 *

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