Developing Minimum Message Length and Support Vector Machine methods to predict user behaviour.

  • Albrecht, David, (Primary Chief Investigator (PCI))
  • Dowe, David (Chief Investigator (CI))
  • Ting, Kai Ming, (Chief Investigator (CI))
  • Kowalczyk, Adam (Partner Investigator (PI))

    Project: Research

    Project Description

    Predicting and modelling customer behaviour enables considerable savings in the telecommunications industry and elsewhere. The resulting predictive models facilitate identifying novice users, identifying fraud, responding to users' needs, guiding and advising users, and forwarding useful information. We consider two cutting-edge data mining approaches, Minimum Message Length (developed and led by Monash) and Support Vector Machines, in order to create efficient tailor-made software. Our software will respond to specific groups of users, and their changes over time, rather than just the average user. Moreover, it will integrate the functionalities of existing individual data mining software.
    StatusFinished
    Effective start/end date31/01/031/09/06

    Funding

    • Australian Research Council (ARC): AUD138,512.00
    • Australian Research Council (ARC)
    • Telstra Corporation Limited: AUD30,000.00
    • Telstra Corporation Limited
    • Monash University