Experiences in building a tool for navigating association rule result sets
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Date
2004
Authors
Fule, Peter
Roddick, John Francis
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Publisher
Australian Computer Society
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Abstract
Practical knowledge discovery is an iterative process.
First, the experiences gained from one mining run
are used to inform the parameter setting and the
dataset and attribute selection for subsequent runs.
Second, additional data, either incremental additions
to existing datasets or the inclusion of additional attributes
means that the mining process is reinvoked,
perhaps numerous times. Reducing the number of
iterations, improving the accuracy of parameter setting
and making the results of the mining run more
clearly understandable can thus significantly speed up
the discovery process.
In this paper we discuss our experiences in this
area and present a system that helps the user to
navigate through association rule result sets in a
way that makes it easier to find useful results from a
large result set. We present several techniques that
experience has shown us to be useful. The prototype
system – IRSetNav – is discussed, which has
capabilities in redundant rule reduction, subjective
interestingness evaluation, item and itemset pruning,
related information searching, text-based itemset
and rule visualisation, hierarchy based searching
and tracking changes between data sets using a
knowledge base. Techniques also discussed in the
paper, but not yet accommodated into IRSetNav,
include input schema selection, longitudinal ruleset
analysis and graphical visualisation techniques.
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Citation
Fule, P. & Roddick, J.F., 2004.
Experiences in building a tool for navigating association rule result sets. Proceedings
of the Australian Workshop on Data Mining and Web Intelligence: ACSW Frontiers,
103-108.