EVALUATING GENERALIZED ASSOCIATION RULES COMBINING

OBJECTIVE AND SUBJECTIVE MEASURES AND VISUALIZATION

Magaly Lika Fujimoto

, Veronica Oliveira de Carvalho

and Solange Oliveira Rezende

Computer and Mathematics Science Institute, S

ao Paulo University, S

ao Carlos, SP, Brazil

Informatics Faculty, Oeste Paulista University, Presidente Prudente, SP, Brazil

Keywords:

Generalized association rules, Objective measures, Subjective measures, Visualization, Knowledge evaluation.

Abstract:

Considering the user view, many problems can be found during the post-processing of association rules, since

a large number of patterns can be obtained, which complicates the comprehension and identiﬁcation of inter-

esting knowledge. Thereby, this paper proposes an approach to improve the knowledge comprehensibility and

to facilitate the identiﬁcation of interesting generalized association rules during evaluation. This aid is real-

ized combining objective and subjective measures with information visualization techniques, implemented on

a system called RulEE-GARVis.

1 INTRODUCTION

One of the data mining problems is that many algo-

rithms generate a large number of patterns, especially

when the association mining technique is used. To

overcome this problem, in relation to the association

rules, a specialized knowledge can be transformed

into a more general concept. This can be realized if an

application domain knowledge exists. The knowledge

can be represented in different manners, for exam-

ple, by taxonomies. Thus, the generalized association

rules, composed by items contained in any level of a

given taxonomy, introduced in (Srikant and Agrawal,

1995), can be obtained in the different steps of the

data mining process, given a general view of the do-

main.

There are many other post-processing techniques

that are used to facilitate the pattern comprehension

and the identiﬁcation of interesting knowledge as

query evaluation, evaluation measures and informa-

tion visualization. The knowledge evaluation mea-

sures are usually classiﬁed as objective or subjec-

tive. The objective measures depend exclusively on

the pattern structure and the data used in the process

of knowledge extraction, while the subjective mea-

sures depend fundamentally on the ﬁnal user’s interest

and/or needs. Therefore, the objective measures are

more general and independent of the domain in which

the data mining process is carried out. These mea-

sures can be insufﬁcient to identify interesting rules,

because the objectives and the specialists’ knowledge

are not considered. Thereby, the combined use of

objective and subjective measures exploits the advan-

tages of each type, improving the identiﬁcation of in-

teresting knowledge (Sinoara and Rezende, 2006).

As well as the knowledge evaluation measures, the

information visualization techniques can also aid the

rules evaluation. The visualization techniques usu-

ally use the human capability of visual interpretation

and assist in knowledge comprehension. According

to (Card et al., 1999), the information visualization is

the use of visual representation, interactive and com-

puter supported, of abstract data to broader cognition.

There are many works found in literature, related

to association rules visualization, as (Hofmann et al.,

2000), (Ong et al., 2002), (Yang, 2005), (Bruzzese

and Buono, 2004), (Techapichetvanich and Datta,

2005), (Chakravarthy and Zhang, 2003), (Ertek and

Demiriz, 2006), (Blanchard et al., 2003). However,

these works do not visualize generalized association

rules (with exception to (Yang, 2005)) and only use

the support and conﬁdence objective measures values.

(Melanda and Rezende, 2003) work does not visual-

ize generalized association rules either, but uses other

objective measures, while other works utilize subjec-

tive measures, as (Liu et al., 2000). (Sinoara and

Rezende, 2006) present an approach that combines

the (Melanda and Rezende, 2003) objective measures

approach with the (Liu et al., 2000) subjective mea-

sures approach, verifying that the combined use of

objective and subjective measures can be interesting

to the user.

285

Fujimoto M., de Carvalho V. and Rezende S. (2009).

EVALUATING GENERALIZED ASSOCIATION RULES COMBINING OBJECTIVE AND SUBJECTIVE MEASURES AND VISUALIZATION.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

285-288

DOI: 10.5220/0001852802850288

 SciTePress

Despite the fact that the generalized association

rules enable the knowledge exploration in different

levels of abstraction, there still is the need to ﬁnd

a way to explore the potential of this kind of rules.

Thus, the main objective of this work is to aid the user

in the comprehension and identiﬁcation of interesting

generalized association rules. Therefore an approach

is proposed to improve the advantages of combining

objective and subjective measures with information

visualization techniques.

2 GARVis: AN APPROACH TO

EVALUATE GENERALIZED

ASSOCIATION RULES

The GARVis approach allows the user to analyze,

through objective measures, a generalized association

rule set using SQL queries and graphic analysis, se-

lecting a subset of rules to be explored by a domain

expert. From the selected rules, subjective measures

can be computed and a subjective measure explo-

ration can be done. That way, the user can analyze a

set of rules observing different aspects. The approach

is divided in four steps, described as follows.

Objective Analysis (1): it is considered, in this step,

that the user already has a generalized association rule

set obtained by the approach proposed by (Carvalho

et al., 2007). Initially, SQL queries are carried out

aiming to select some features and/or objective mea-

sure values that are of application interest in order to

obtain a focus set (the objective measures considered

are the ones described in (Tan et al., 2004)). If the

user is not interested in speciﬁc items, the focus set

is formed by the whole rule set. Applying graphic

analysis on the focus set using objective measures,

the focus set is ﬁltered, conducing to the identiﬁca-

tion of a subset of potentially interesting rules (PIR).

The graphic analysis of this step is realized in an in-

teractive way in a X-Y graphic, enabling the user to

directly interact with the graphic, facilitating his/her

comprehension and usability.

PIR Subset Evaluation (2): has as input the PIR sub-

set obtained from Step 1 and has as objective the eval-

uation of these rules by a domain expert. The knowl-

edge expressed in each one of the rules is classiﬁed

as irrelevant, obvious, previous, unexpected or use-

ful (one or more classiﬁcation can be selected). Dur-

ing evaluation, the rules considered as irrelevant, by

a domain expert, are eliminated from the focus set,

as all the other similar rules (rules that contain irrel-

evant items in the same position (same side) of the

rule classiﬁed as irrelevant). In cases where there

is a rule with the same items, but in different posi-

tions, the user is asked to check if the rule has also

to be considered irrelevant. During this process, the

user can, in parallel, visualize the rules in a textual

form and make a graphic analysis. In this step, X-Y

graphics and bar charts are available for rule visual-

ization. The user can also visualize redundant, com-

plements and exceptions rules, which can be added

in the PIR subset in order to be evaluated. The de-

ﬁnitions of redundancy and exception described in

the (Zaki, 2004) and (Gonalves et al., 2005) works,

are considered respectively. The complement deﬁni-

tion is proposed in this work as follows. Consider

R = {r

, ..., r

} a set of rules and X = {x

, ..., x

} a

taxonomy set. Given that r

∈ R, r

is a comple-

ment of r

if ((r

.LHS = r

.LHS ∧ r

.RHS\r

.RHS =

w ∧ r

.RHS \ r

.RHS = w

) ∨ (r

.RHS = r

.RHS ∧

.LHS \ r

.LHS = w ∧ r

.LHS \ r

.LHS = w

)) and

w, w

have the same ancestral in taxonomy x

. The

complement is symmetric, so r

is a complement of r

and r

is a complement of r

Subjective Processing (3): in this step, for each of

the rules that are not eliminated from the focus set,

the subjective measures conforming, unexpected an-

tecedent, unexpected consequent, and both-side un-

expected, deﬁned by (Liu et al., 2000), are computed.

To compute these measures, the classiﬁcations made

in Step 2, for the rules contained in the PIR subset, are

used as domain knowledge. That way, it is possible to

carry out an analysis with the subjective measures to

aid the identiﬁcation of possible interesting rules not

previously found only by the objective measures anal-

ysis.

Subjective Measures Analysis (4): during the ex-

ploration of the rules contained in the resultant focus

set, using the subjective measures computed in Step

3, the user has the support of graphic analysis using

X-Y graphics and bar charts. The aim, with these vi-

sualizations, is to increase the rule comprehensibil-

ity and to facilitate the identiﬁcation of interesting

knowledge, since the user has a visual and interactive

exploration option. It is important to mention that the

exploration in the resultant focus set should be car-

ried out according to the goals of the user during the

analysis. For example, if the user wishes to conﬁrm

his/her previous knowledge, he/she can use the con-

forming measure and list the rules that conform to

the rules that had been evaluated as obvious or pre-

vious knowledge in Step 2. During this evaluation the

user can ﬁnd some rules, not previously found, that

are also interesting for him/her.

After applying the four steps, the approach gener-

ICEIS 2009 - International Conference on Enterprise Information Systems

286

(a) PIR subset selection. (b) PIR subset evaluation.

Figure 1: Examples of PIR subset selection and evaluation process.

ates as output a subset containing the interesting rules

identiﬁed during the process, subset composed by the

rules found in the PIR subset plus the rules found in

the focus set that were considered interesting accord-

ing to the subjective analysis.

3 EXPERIMENT AND SCREEN

SNAPSHOTS

Experiments were carried out in order to demonstrate

the viability and usefulness of the GARVis approach,

using a system, called RulEE-GARVis, that imple-

ments the proposed approach. This section presents

one of the experiments realized with the groceries

data set (available for download at http://www.r-

project.org/) that contains one month of real-world

point-of-sale from a typical grocery outlet. A set com-

posed by 1680 generalized association rules, used in

this experiment, was obtained by the approach pro-

posed by (Carvalho et al., 2007).

In the objective analysis (Step 1) a focus set was

selected composed by 459 rules with Interest Factor

values greater than 2. Analyzing these rules (Fig-

ure 1(a)) with the IS/Cosine, Jaccard, φ-coefﬁcient,

Piatetsky-Shapiro’s and Kappa objective measures

(according to (Carvalho et al., 2007), the more ap-

propriate measures to be used in the evaluation of

rules containing generalized items in the antecedent)

12 rules were selected to compose the subset of po-

tentially interesting rules (PIR).

Analyzing the PIR subset (Step 2), the domain ex-

pert classiﬁed the knowledge expressed in each one of

the 12 rules as unexpected, useful, obvious, previous

and/or irrelevant (Figure 1(b)). During this evalua-

tion, the user could visualize the positive and negative

exceptions, complements and redundancies, in cases

of existence. Using X-Y graphics, similar to Fig-

ure 1(a), and bar charts combined with some objec-

tive measures, of the 12 PIR, 9 were evaluated as in-

teresting and 3 as not interesting since they presented

a previous knowledge.

After the step referring to the subjective mea-

sures computation (Step 3), the subjective analysis

was made (Step 4). In this step, the user could list

in a textual form the evaluated rules, separated ac-

cording to its classiﬁcation made in Step 2. The user

could also visualize in a X-Y graphic the evaluated

rules using different colors, where each color indi-

cated its classiﬁcation made in Step 2. After selecting

one of the rules in the graphic, the user chose some

subjective measures, deﬁning its minimum and maxi-

mal values, in order to search in the resultant focus

set the rules that corresponded to the knowledge ex-

pressed by the selected rule (deﬁned in Step 2). From

the obtained set, the user visualized the rules in a tex-

tual form and deﬁned some of them as interesting.

Thus, after the selection and analysis of some rules,

3 interesting rules were selected in this step.

In the end, the user visualized the rules considered

interesting in a textual form and through bar and pie

charts. In this experiment, 12 interesting rules were

found. It is important to mention that 9 of these rules

were found using objective measures and 3 using sub-

jective measures, demonstrating the importance of

combining both measures supported by visualization.

It could be observed, through this experiment, that

EVALUATING GENERALIZED ASSOCIATION RULES COMBINING OBJECTIVE AND SUBJECTIVE MEASURES

AND VISUALIZATION

287

the approach is viable, since it aids the user in the

comprehension and identiﬁcation of interesting gen-

eralized association rules, allowing the user to explore

and evaluate the rules using many resources. Besides,

the interactive graphic analysis facilitates the rule set

exploration, since this analysis is made with different

measures and ﬁlters.

4 CONCLUSIONS

This paper presented an approach that aids the post-

processing of generalized association rules and facili-

tates the comprehension and the identiﬁcation of the

interesting ones using objective and subjective mea-

sures combined with information visualization tech-

niques, features that distinguish the approach with the

ones cited in Section 1. This combination provides

the user with different evaluation mechanisms, facili-

tating his/her participation in the discovery process of

interesting knowledge.

The experiment presented in Section 3 shows that

the application of the proposed approach using the

RulEE-GARVis system is viable and useful. As future

work, other experiments using the GARVis approach

with different real data sets and specialists in other

domains will be carried out.

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