Association mining of dependency between time series using Genetic Algorithm and discretisation
Mourad Ykhlef · International Journal of Business Intelligence and Data Mining · 2011
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).