The reason for data mining is to generate rules that can be used as the basis for making
decisions. One such area is sequence mining which, in terms of transactional datasets,
can be stated as the discovery of inter-transaction associations or associations between
different transactions. The data used for sequence mining is not limited to data stored
in overtly temporal or longitudinally maintained datasets and in such domains data can
be viewed as a series of events, or episodes, occurring at specific times. The problem
thus becomes a search for collections of events that occur frequently together.
While the mining of frequent episodes is an important capability, the manner in
which such episodes interact can provide further useful knowledge in the search for a
description of the behaviour of a phenomenon but as yet has received little investigation.
Moreover, while many sequences are associated with absolute time values, most
sequence mining routines treat time in a relative sense, returning only patterns that
can be described in terms of Allen-style relationships (or simpler), ie. nothing about
the relative pace of occurrence. They thus produce rules with a more limited expressive
power. Up to this point in time temporal interval patterns have been based on
the endpoints of the intervals, however in many cases the ‘natural’ point of reference is
the midpoint of an interval and it is therefore appropriate to develop a mechanism for
reasoning between intervals when midpoint information is known.
This thesis presents a method for discovering interacting episodes from temporal
sequences and the analysis of them using temporal patterns. The mining can be conducted
both with and without the mechanism for handling the pace of events and
the analysis is conducted using both the traditional interval algebras and a midpoint
algebra presented in this thesis.
The visualisation of rules in data mining is a large and dynamic field in its own right
and although there has been a great deal of research in the visualisation of associations,
there has been little in the area of sequence or episodic mining. Add to this the emerging
field of mining stream data and there is a need to pursue methods and structures for
such visualisations, and as such this thesis also contributes toward research in this
important area of visualisation.
| Identifer | oai:union.ndltd.org:ADTP/216420 |
| Date | January 2007 |
| Creators | Mooney, Carl Howard, carl.mooney@bigpond.com |
| Publisher | Flinders University. Informatics and Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.flinders.edu.au/disclaimer/), Copyright Carl Howard Mooney |
Page generated in 0.0017 seconds