INTERACTIVE DATAMINING PROCESS BASED ON

HUMAN-CENTERED

SYSTEM FOR BANKING MARKETING

APPLICATIONS

Olivier Couturier, Engelbert Mephu Nguifo

CRIL CNRS FRE 2499 - Universit

e d’artois

rue Jean Souvraz, SP 18, F-62307 Lens Cedex, France

Brigitte Noiret

Caisse d’Epargne du Pas de Calais (CEPDC)

1, place de la r

epublique, B.P. 199, F-62304 Lens Cedex, France

Keywords:

Human-Computer Interaction, Knowledge Discovery in Databases (KDD), Association Rules Mining, Bank-

ing Marketing.

Abstract:

Knowledge Discovery in Databases (KDD) is the new hope for marketing due to the increasing collection of

large databases. There is a paradox because the companies must improve the development policy of customer

loyalty by using methods that do not allow to treat large quantities of data. Our current work is the results of

a study that we led on a association rules mining in banking marketing problem. Our ﬁrst encouraging results

steered our work towards a hierarchical association rules mining, using a user-driven approach rather than an

automatic approach. The user is at the heart of the process, playing a role of evolutionary heuristic. Mining

process is oriented according to intermediate expert’s choices. The ﬁnal aim of our approach is to use the

advantages of the methods to decrease both number of generated rules and expertise time. This paper presents

the results of our research step for including the user into datamining process.

1 INTRODUCTION

Marketing process is a social and ﬁnancial mecha-

nism in which people satisfy their needs and desires

by creating and exchanging products or other entities.

The purpose is to establish satisfactory relations be-

tween customers and suppliers in order to preserve

the trade preference. The ﬁnal aim is to go towards

a network which constitutes the effective capital of

the companies. The sale of new products or main-

tenance products is a necessary step in order to se-

cure customer loyalty. The problem is to anticipate

their choices so as to predict the products which they

might ﬁnd interesting. There are two categories of

customers. First of all, the customers whom the com-

pany wishes to keep as a customer and secondly, those

from the other banks. For the ﬁrst case, the term

which is usually used is defensive marketing while

for the second case, the corresponding term is offen-

sive marketing. Our work is oriented towards the ﬁrst

problem.

Let P , be the population and S, be a sub-

population. The problem is to ﬁnd the better sub-

population S in P depending on the initial problem.

The implementation of a campaign on launching a

new product can be costly depending on the number

of people who must be contacted. The main idea is to

have a maximum of positive answers in order to re-

duce expenses. It is necessary to be able to offer the

products as well as possible by using the database his-

tory. Indeed, databases contain a signiﬁcant quantity

of knowledge which is hidden in meaningful masses.

The association rules mining is one of the possible so-

lutions allowing to solve the problem by establishing

logical relations between products. The next section

describes association rules mining.

As the number of large databases increasing, ex-

tracting useful information is a difﬁcult and opened

problem. This is the goal of an active research domain

: Knowledge Discovery in Databases (KDD) (Fayyad

et al., 1996). We focus on association rules mining

(Agrawal et al., 1996) such as “If Antecedent then

Conclusion”. Several works were published which

are based on two main indices to measure the qual-

ity of a rule: support and conﬁdence (Agrawal et al.,

1996). Two main limitations occurs from these works.

104

Couturier O., Mephu Nguifo E. and Noiret B. (2005).

INTERACTIVE DATAMINING PROCESS BASED ON HUMAN-CENTERED SYSTEM FOR BANKING MARKETING APPLICATIONS.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 104-109

DOI: 10.5220/0002550301040109

 SciTePress

First, the support does not allow to extract speciﬁc

information valid on a small number of transactions

in the database. Trivial information may already be

known by the user. The calculation of this informa-

tion has a high time cost. This type of approach is an

automatic approach.

Secondly, the number of generated rules is too sig-

niﬁcant and leads to another problem called “Knowl-

edge Mining” (Blanchard et al., 2003). Expertise time

is costly and it is not taken into account in studies.

Indeed, if the support decreases, the number of gen-

erated rules increases. The solution consists in using

quality measures to rate the rules but the experience of

the expert is not taken into account either. In order to

exploit his tacit knowledge, hierarchical association

rules mining can be used, in order to generate knowl-

edge with various levels of granularity (Han and Fu,

1995; Hipp et al., 1998; Srikant and Agrawal, 1997;

Srikant et al., 1997; Tseng, 2001).

In a real-expert datamining process, it is not always

possible to formalize the tacit knowledge of an expert

in order to optimize the rules because he does not al-

ways know which kind of rules he would like to ob-

tain. To solve it, the expert is introduced while the

process is ongoing. Our approach deals with this case.

It is called a user-driven approach and it gives the

main role of evolutionary heuristic to the user (Kuntz

et al., 2000). The main idea of our approach is to use

the advantages of the two methods to decrease both

expertise time and number of generated rules. The hi-

erarchical association rules mining is used to decrease

the number of generated rules and the user-driven ap-

proach is used to reduce trivial rules.

Section 2 recalls association rules mining and gives

a brief overview of both automatic and user-driven ap-

proaches. Section 3 describes our hybrid approach,

and experimental results are presented in section 4.

2 ASSOCIATION RULES MINING

Association rules mining (Agrawal et al., 1996) can

be divided into two subproblems: the generation of

the frequent itemsets lattice and the generation of as-

sociation rules. The complexity of the ﬁrst subprob-

lem is exponential. Let |I| = m the number of

items, the search space to enumerate all possible fre-

quent itemsets is equal to 2

, and so exponential in m

(Agrawal et al., 1993).

2.1 Problem

Let I = {a

, a

, ..., a

} be a set of items, and let

T = {t

, t

, ..., t

} be a set of transactions etablish-

ing the database, where every transaction t

is com-

posed of a subset X ⊆ I of items where each trans-

action has a unique identiﬁer, called T ID. A set of

items X ⊆ I is called itemset. A subset of items

X ⊆ I is called a k-itemset. A transaction t

contains

an itemset X in I, if X ⊆ t

. The support of an item-

set X, noted σ(X), is the percentage of transactions

contained in T in which X is a subset :

support(X) =

|{t ∈ T |X ⊆ t}|

|t ∈ T |

An itemset is frequent if the support σ(X) ≥

minsup, where minsup is the user-speciﬁed minimum

support. An association rule is an implication such

as X

7→ X

, where X

and X

are itemsets with

, X

⊆ I and X

∩ X

= ∅. The support of an

association rule r: X

7→ X

is equal to the support

of the union of itemsets which establish it (Agrawal

et al., 1993) :

support(r) = support(X

∪ X

)

The conﬁdence of an association rule is the condi-

tional probability that the transaction contains X

knowing X

conﬁdence(r) =

support(r )

support(X

)

Rules that satisfy both a minimum support threshold

and a minimum conﬁdence threshold are called strong

rules. The following subsection presents automatic

and user-driven approaches.

2.2 Automatic approach vs

user-driven approach

Association rules mining is an automatic task where

the user appears at the beginning and at the end within

the process. First of all, he determines the support

and the conﬁdence of the algorithm. Once the mining

ended, he rates the results obtained. The main prob-

lem of this method is the large number of generated

rules (Kuntz et al., 2000). To solve it, the user-driven

approach applied to association rules mining was pro-

posed. The last two stages of KDD: datamining and

post-processing was grouped. The user is at the heart

of mining and he can drive it throughout the process

(Kuntz et al., 2000). This is the main difference with

automatic approach. The main problem of this ap-

proach is to present comprehensive and fast results so

that the user does not waste his time analyzing cur-

rent results. Various works on rules ﬁltering by qual-

ity criteria were proposed (Ohsaki et al., 2004) but the

experience of the user is not exploited during all the

KDD process. We present related works in the next

subsection.

INTERACTIVE DATAMINING PROCESS BASED ON HUMAN-CENTERED SYSTEM FOR BANKING

MARKETING APPLICATIONS

105

2.3 Related works

All techniques currently developped have a common

purpose which is to discover association rules in data-

bases. One of the major problems of association rules

mining is the large number of patterns which are gen-

erated. It is difﬁcult for the expert to identify those as-

sociation rules that are interesting for him. To help the

expert to choose among these patterns, several works

were proposed on association rules ﬁltering accord-

ing to their interestingness (Liu et al., 1996). Other

works are proposed on hierarchical association rules

mining. Let I and T as previously presented, and

let G, a taxonomy or hierarchical tree. A taxonomy

is a directed acyclic graph (DAG) on the items in I

where items are the leaves and where edges are in-

heritance relation. This approach allows to generate

multi-level association rules (Han and Fu, 1995; Hipp

et al., 1998; Srikant and Agrawal, 1997; Srikant et al.,

1997; Tseng, 2001).

In these works, there are two different kind of rel-

evance measures. The ﬁrst are called objective mea-

sures (Silberschatz and Tuzhilin, 1995). These mea-

sures are data-oriented (Bayardo and Agrawal, 1999;

Tan et al., 2002; Hilderman and Hamilton, 2001).

Several of these works are efﬁcient to discover the

best rules or to estimate the best rules (Morimoto

et al., 1998) thanks to one of these objective measures

but they are limited. Indeed, there are no measures

which are able to treat efﬁciently random problems.

They are successful in particular contexts. Further-

more, they do not allow yet to rate patterns quality

(Liu et al., 1996). A comparison of these measures

was done in (Ohsaki et al., 2004).

The second measures are called subjective mea-

sures. These second measures are user-oriented (Pad-

manabhan and Tuzhilin, 1998; Liu et al., 1996). The

expert’s tacit knowledge, in a research domain, are

taken into account. Contrary to objective measures

which are numerous, subjective measures is a recent

research domain. We can count a dozen measures

and split them into three sub-groups: unexpected

(Matheus et al., 1996; Klemettinen et al., 1994; Sil-

berschatz and Tuzhilin, 1996; Liu et al., 1996; Liu

et al., 1997; Padmanabhan and Tuzhilin, 1999; Liu

et al., 2000; Shekar and Natarajan, 2002), actionabil-

ity (Klemettinen et al., 1994; Piatetsky-Shapiro and

Matheus, 1994; Freitas, 1999) and anticipation (Rod-

dick and Rice, 2001). These different measures ex-

ploit the knowledge of the expert to confront them

with the results of datamining. The aim is to highlight

useful and more or less unexpected knowledge. In this

paper, we focus on this second family of measures.

The conception of subjective measure is a difﬁcult

task for several reasons. First, experts have not com-

mon interests for a common research domain. Sec-

ondly, given a database, and a knowledge set, differ-

ent experts may be interested in different subsets of

this explicit knowledge. Finally, the expert’s conclu-

sions can vary depending on time and situation. These

various points show how complex the problem is. We

present in the next section, our new method based on

a combination of both user-driven and hierarchical as-

sociation rules mining approaches.

3 HYBRID ALGORITHM FOR

ASSOCIATION RULES MINING

3.1 Algorithm details

First of all, item taxonomy must be created in coop-

eration with the expert of the domain. The aim is to

develop the taxonomy by grouping items possessing

common roots. The rule will be specialized at the fol-

lowing level according to the expert’s choices. The

current association rule mining consists in building a

lattice of frequent itemsets. In our example, the lat-

tice is created depending on level 4 of the taxonomy,

that is the lowest level. We are working on taxonomy

level. The starting level is the highest one (cf Fig 1

(a)).

∅

A ∨ B ∨ C ∨ D ∨ E ∨ F ∨ G ∨ H

∅

A ∨ B ∨ C ∨ D E ∨ F ∨ G ∨ H

A ∨ B ∨ C ∨ D ∧ E ∨ F ∨ G ∨ H

(a) Level 1 (b) Level 2

Figure 1: Lattice generated at levels 1 and 2

But, association rules are not generated with a sin-

gle itemset. Consequently, the starting level will al-

ways be level 2 (cf Fig 1 (b)). Thenceforth, there are

two possibilities. First of all, association rules are not

generated. In this case, mining is restarted from the

next level. Secondly, rules are generated depending

on level 2 and proposed to the expert. He selects rele-

vant rules for him. The taxonomy is pruned according

to this selection. The corresponding itemsets are kept

∅

A ∨ B C ∨ D E ∨ F G ∨ H

A ∨ B ∧ C ∨ D A ∨ B ∧ E ∨ F A ∨ B ∧ G ∨ H C ∨ D ∧ E ∨ F C ∨ D ∧ G ∨ H E ∨ F ∧ G ∨ H

A ∨ B ∧ C ∨ D ∧ E ∨ F A ∨ B ∧ C ∨ D ∧ G ∨ H A ∨ B ∧ E ∨ F ∧ G ∨ H C ∨ D ∧ E ∨ F ∧ G ∨ H

A ∨ B ∧ C ∨ D ∧ E ∨ F ∧ G ∨ H

Figure 2: Lattice generated at level 3

ICEIS 2005 - HUMAN-COMPUTER INTERACTION

106

in the taxonomy, and other itemsets and their inherited

items are pruned (Hipp et al., 1998). We consider, for

our example, that for this level, the taxonomy was not

pruned. Mining is then achieved at the next level (cf

Fig 2). As previously, rules are generated at level 3

(cf Fig 2). For instance, the expert selects one rule “If

A ∨ B ∧ G ∨ H 7→ E ∨ F ”. The itemset C ∨ D does

not appear in this rule, and will then be pruned from

the taxonomy as well as the inherited items C and D

(cf Fig 3) (Hipp et al., 1998).

A ∨ B ∨ C ∨ D ∨ E ∨ F ∨ G ∨ H

A ∨ B ∨ C ∨ D

A ∨ B

A B

C ∨ D

C D

E ∨ F ∨ G ∨ H

E ∨ F

E F

G ∨ H

G H

Level 1

Level 2

Level 3

Level 4

Figure 3: Item taxonomy

If no relevant aggregated rules are found, mining

is restarted from the last level of the taxonomy, as

classical association rules mining. But, for our ex-

ample, there is no more than 2

at the beginning but

thanks to the taxonomy pruning. The main advan-

tage of this method is the variation of space and time

complexities at the search level. If we increase this

search level, running time are going to decrease. The

useless or coarse association rules will be discovered

from the beginning and they are not developed in the

next levels. The number of generalized rules will be

more or less low. This algorithm uses a ﬁxed sup-

port. This approach allows to generate mono-level

rules. We present our algorithm in the next subsec-

tion.

3.2 Search Hierarchical Association

Rules for Knowledge (SHARK)

SHARK algorithm is described hereafter (see algo-

rithm 1). The Update function is not detailed because

it is a simple prunning of the items for the current

level. The RuleGeneration() function is not detailed

too in this paper because this function doesn’t present

a new idea. It runs Apriori which is a parameter that

can be replaced by any other rule generation algo-

rithm. Starting at level i, the algorithm updates the

set of valid items at this level. Given the minsup,

a search for frequent itemsets is achieved with the set

of selected items. A procedure RuleGeneration is run,

given a conﬁdence thereshold, to generate a rules set.

This rules set is analyzed by the expert which selects

relevant ones. Items which are not present among rel-

evant rules are removed from the items list. The pro-

cedure restarted at the next level i + 1 until the last

level is reached. We have implemented a datamin-

ing platform, Lminer, in which both Apriori (Agrawal

et al., 1996) and SHARK are included. SHARK is an

extension of Apriori algorithm that integrates our hy-

brid approach. We present in the next section experi-

mental results.

Algorithm 1: SHARK Algorithm

function AssociationRuleGenerate(Set of items I,

Database T , Taxonomy G)

Data: Set containing all the items,

Database containing all the transactions,

Item taxonomy

Result: Set of association rules F

begin

Level=1;

while NextLevel==OK and user!=END do

// Increase level

Level++;

// Updating the set of items depending on the

//search level

I ← Update(I,Level);

// Frequent itemsets search

← FrequentSearch(I,T ,G,minsup);

// Generation of association rules

← RuleGeneration(F

,G,minconf);

// Presentation of rules to the expert

raF inal

← ChooseRule(F

);

// Pruning taxonomy

PruneTaxonomy(G,F

raF inal

);

// End test

if Level==MaxLevel then

NextLevel==NotOK;

return F

raF inal

;

end

4 RESULTS

Our purpose is to include a hierarchical mining and a

user-driven approach. We don’t want propose a new

vizualisation methodology. We developed an easy-

to-use graphical interface for our experiments. In-

deed, extracting nuggets is very difﬁcult when the rel-

evant information is hidden in a large amount of data.

Various works already exist to help expert analysis

(Klemettinen et al., 1996; Liu et al., 1999). These

two works were completed by several works for rules

exploration (Blanchard et al., 2003; Ben-Yahia and

Mephu-Nguifo, 2004). A set of association rules is

proposed to the expert at each level. He chooses the

rules which he ﬁnds relevant just with a mouse click

then he runs the mining to the following level with a

button and so on.

First of all, we generated the taxonomy with the ex-

perts. The example shows a balanced tree (cf Fig 3)

but it is not always balanced. It has no inﬂuence on

the algorithm running. When the leaves are not on

the last level of the taxonomy, the algorithm selects

INTERACTIVE DATAMINING PROCESS BASED ON HUMAN-CENTERED SYSTEM FOR BANKING

MARKETING APPLICATIONS

107

all the leaves on the previous levels. Our taxonomy

is composed of 114 nodes and 57 leaves. To esti-

mate the rules, a graphical interface was developed

to hold some speciﬁc items. This tool allows to vary

several metrics: absolute or relative support rate, con-

ﬁdence rate, number of rules, number of items of the

antecedent and the conclusion and to select speciﬁc

items of the antecedent and the conclusion. Thanks to

this tool, the number of rules was reduced from 2445

to few dozens according to the selection of three ex-

perts working together. It is the reason which explains

the short expertise time. We tested our data with Apri-

ori (automatic approach) and with Shark (user-driven

approach). Our benchmark is composed of 57 items

(the last level of the taxonomy) and 400 000 trans-

actions. We work with a pentium III 700 Mhz with

128 Mo RAM. The metrics minsup and minconf

are ﬁxed by the experts. Our results are presented in

table 1.

Table 1: Experiment results

Nb rules Calcul Sort Expert Select

Apriori 2445 1140s 56 600s 10

Shark (L1) ∅ ∅ ∅ ∅ ∅

Shark (L2) 5 1s 2 30s 2

Shark (L3) 546 70s 16 60s 6

Shark (L4) 2113 1080s 36 300s 8

Shark 2113 1151s 36 390s 8

Apriori generates 2445 rules in 1140 seconds. This

time does not include the expertise time. It is nec-

essary to add about 600 seconds for a total of 1740

seconds. Shark algorithm works on more and less ag-

gregated data. At the ﬁrst level, no rule is generated.

The mining begins on the second level. Shark gener-

ates 5 rules in 1 second and 30 seconds of expertise

time. At the ﬁrst level, 546 rules generated in 70 sec-

onds and 60 seconds of expertise time. Finally, at the

last level, as Apriori algorithm, 2113 rules are gen-

erated in 1080 seconds and 300 seconds of expertise

time. It is necessary to note that this results vary ac-

cording to the material used.

The total running time of the algorithm is 1151 sec-

onds and 1140 seconds for Apriori. Shark is longer

than Apriori. It is because the experts uses a inter-

active graphical interface. A mining on a level de-

pends on an interaction between the experts and the

interface. As long as he does not click on a button to

calculate the next level, mining is stopped. It is the

reason why the running time of fewer rules are longer

with Shark algorithm. Anyway, our main problem is

not the running time but the expertise time. The to-

tal expertise time with Shark (390s) is shorter than

Apriori (600s). The number of rules with SHARK

(8 rules) is smaller than the number obtained with

Apriori (10 rules). The two rules was pruned accord-

ing to experts’ aims. The conﬁdence of these two

rules were very low, and the experts qualiﬁed them

as noise among the initial set of selected rules. Fur-

thermore in our experiments, the experts was able to

obtain a gain of 35% of the expertise time when using

SHARK, while the number of selected rules remains

almost signiﬁcantly identical. However, this obser-

vation should be validated on different other applica-

tions and with different experts. In fact, the loss of

rules during the whole process had to be minimized

to increase the efﬁciency of the method. And this is

also linked to expert’s choices at each level.

5 CONCLUSIONS AND FUTURE

WORKS

The user-driven approach allows to moderate two

problems. On the one hand, the limitations of expert

reﬂexion and on the other hand, the problem of data

volume which does not allow to obtain quickly rele-

vant results by automatic approach (Silberschatz and

Tuzhilin, 1996). In this paper, we have presented our

work about a hybrid method based on a hierarchical

association rules mining and a user-driven approach.

The main idea is to decrease the expertise time and

the number of generated rules. Hierarchical associ-

ation rule mining is used to decrease expertise time.

Mining is performed level by level and is oriented by

expert’s choices. The direct consequence is that the

number of generated rules is small and these rules are

better targeted because the expert directs the mining

during all the process.

The experiments show that hybridizing the hierar-

chical association rules mining and the user-driven

approach is interesting especially in our applica-

tion case of banking marketing. Indeed, the Shark

methodology allowed to decrease the total expertise

time and the number of rules. A KDD process must

be steered according to the initial purposes of the ex-

pert. Thus, his role is really essential before, after,

but especially during the process. One limitation of

our approach is that the number of generated rules re-

mains nevertheless large. We reduce the problem but

it needs to be improved. Another limitation is that

we deﬁne a taxonomy with simple inheritances. We

don’t treat the multiple inheritances as in (Srikant and

Agrawal, 1997).

ACKNOWLEDGMENTS

This work has been partly supported by the “Cen-

tre National de la Recherche Scientiﬁque” (CNRS),

the “Association Nationale de la Recherche Scien-

tiﬁque” (ANRT), the “IUT de Lens” and the “Uni-

versit

e d’Artois”. A special thanks to the marketing

service of the “Caisse d’Epargne du Pas de Calais”.

ICEIS 2005 - HUMAN-COMPUTER INTERACTION

108

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