Emergent intertransaction association rules for abnormality detection in intelligent environments
MetadataShow full item record
Copyright © 2005 IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal associationanomalies otherwise not readily detectable by traditional "market basket" intratransaction mining.
Showing items related by title, author, creator and subject.
Berwick, Lyndon (2009)The analytical capacity of MSSV pyrolysis has been used to extend the structural characterisation of aquatic natural organic matter (NOM). NOM can contribute to various potable water issues and is present in high ...
Lorsakul, A.; Li, Q.; Trott, Cathryn; Hoog, C.; Petibon, Y.; Ouyang, J.; Laine, A.; El Fakhri, G. (2014)Purpose: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective ...
Goessmann, Florian (2007)The capability to monitor bushfires on a large scale from space has long been identified as an important contribution to climate and atmospheric research as well as a tool an aid in natural hazard response. Since the work ...