An Improved Approximation Algorithm for Co-location Mining in Uncertain Data Sets using Probabilistic Approach

Abstract

complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networksdata, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining.One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find allcolocations that are to be generated from a random world. For this we first apply an approximation error to find allthe PPCs which reduce the computations. Next find all the possible worlds and split them into two different worldsand compute the prevalence probability. These worlds are used to compare with a minimum probability threshold todecide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected dataset show the significant improvement in computational time in comparison to some of the existing methods used incolocation mining.