On Mining Movement Pattern from Mobile Users

  • Authors:

  • David Taniar;John Goh

  • Affiliations:

  • School of Business Systems, Monash University, Clayton, Vic, Australia;School of Business Systems, Monash University, Clayton, Vic, Australia

  • Venue:
  • International Journal of Distributed Sensor Networks - Heterogenous Wireless Ad Hoc and Sensor Networks
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In the era in which activities performed by mobile users are tracked through various sensing mechanisms, the movement data collected through these sensors is submitted into a data mining algorithm in order to determine the movement pattern. The movement pattern refers to the pattern that mobile users generally take to move from one base location to another base location through multiple intermediate locations. This paper provides a proposal and case study on how the movement pattern can be extracted from mobile users through transforming the user movement database to the location movement database and subsequently transferred to an algorithm Apriori-like movement pattern (AMP) and movement tree (M-tree). The result is a list of sequences in which mobile users frequently go through that which satisfies min-support and min-confidence. The result of this movement pattern mining exercise opens up a new future for the prediction of the movement for the individual mobile user.