Information Technology Reference
In-Depth Information
9
Tree-Based Algorithms for Action Rules
Discovery
Zbigniew W. Ras
1
,
2
, Li-Shiang Tsay
3
, and Agnieszka Dardzinska
4
1
Univ. of North Carolina, Charlotte, Dept. of Computer Science,
9201 Univ. City Blvd., Charlotte, NC 28223, USA
2
Polish-Japanese Institute of Information Technology,
Koszykowa 86, 02-008 Warsaw, Poland
3
North Carolina A&T State Univ., School of Technology,
Greensboro, NC 27411, USA
4
Bialystok Technical Univ., Dept. of Computer Science,
15-351 Bialystok, Poland
Abstract.
One of the main goals in Knowledge Discovery is to find interesting associ-
ations between values of attributes, those that are meaningful in a domain of interest.
The most effective way to reduce the amount of discovered patterns is to apply two
interestingness measures, subjective and objective. Subjective measures are based on
the subjectivity and understandability of users examining the patterns. They are di-
vided into actionable, unexpected, and novel. Because classical knowledge discovery
algorithms are unable to determine if a rule is truly actionable for a given user [1],
we focus on a new class of rules [15], called E-action rules, that can be used not only
for automatic analysis of discovered classification rules but also for hints of how to
reclassify some objects in a data set from one state into another more desired one.
Actionability is closely linked with the availability of flexible attributes [18] used to
describe data and with the feasibility and cost [23] of desired re-classifications. Some
of them are easy to achieve. Some, initially seen as impossible within constraints set
up by a user, still can be successfully achieved if additional attributes are available.
For instance, if a system is distributed and collaborating sites agree on the ontology
[5], [6] of their common attributes, the availability of additional data from remote sites
can help to achieve certain re-classifications of objects at a server site [23]. Action tree
algorithm, presented in this paper, requires prior extraction of classification rules sim-
ilarly as the algorithms proposed in [15] and [17] but it guarantees a faster and more
effective process of E-action rules discovery. It was implemented as system
DEAR
2
.
2
and tested on several public domain databases. Support and confidence of E-action
rules is introduced and used to prune a large number of generated candidates which
are irrelevant, spurious, and insignificant.
9.1
Introduction
Finding useful rules is an important task of knowledge discovery in data. Most
of the researchers focused on techniques for generating patterns, such as classifi-
cation rules, association rules...etc, from a data set. They assume that it is users
responsibility to analyze these patterns and infer actionable solutions for specific
