MINING OF COMPLEX OBJECTS VIA DESCRIPTION

CLUSTERING

Alejandro Garc

ıa L

opez

European Laboratory for Nuclear Research (CERN)

Geneva (Switzerland)

Rafael Berlanga

Depto. de Lenguajes y Sistemas Inform

aticos. Universitat Jaume I

Castell

on (Espa

na)

Roxana Danger

Depto. de Lenguajes y Sistemas Inform

aticos. Universitat Jaume I

Castell

on (Espa

na)

Keywords:

Complex objects, association rules, clustering, data mining.

Abstract:

In this work we present a formal framework for mining complex objects, being those characterised by a set of

heterogeneous attributes and their corresponding values. First we will do an introduction of the various Data

Mining techniques available in the literature to extract association rules. We will as well show some of the

drawbacks of these techniques and how our proposed solution is going to tackle them. Then we will show how

applying a clustering algorithm as a pre-processing step on the data allow us to ﬁnd groups of attributes and

objects that will provide us with a richer starting point for the Data Mining process. Then we will deﬁne the

formal framework, its decision functions and its interesting measurement rules, as well as a newly designed

Data Mining algorithms speciﬁcally tuned for our objectives. We will also show the type of knowledge to be

extracted in the form of a set of association rules. Finally we will state our conclusions and propose the future

work.

1 INTRODUCTION

The problem of mining complex objects, as we under-

stand it, is that of extracting useful information out of

multidimensional heterogeneous data. To fully com-

prehend this concept we need therefore to deﬁne what

we mean by extracting useful information and multi-

dimensional heterogeneous data.

When we talk about multidimensional heteroge-

neous data, we are referring to collections of at-

tributes of different types (boolean, categorical, nu-

merical, etc.) which are represented in an structured

way. This structured representation would normally

be based on a relational schema, although we could

also think of, for example, a collection of XML doc-

uments.

On the other hand, what we mean by extracting

useful information is mainly the discovering of fre-

quent and approximate underlying patterns (Associa-

tion Rules, ARs), which can help users to undertake

a number of decision taking tasks. Examples of these

are: summarizing a data collection, ﬁnding interesting

relations amongst its attributes, ﬁnding certain trends,

etc.

This kind of association rules can be applied to a

wide range of applications. Our main motivating ap-

plication consists of mining large log repositories that

contain data about the performance of a GRID in-

frastructure for ALICE experiments at CERN. Stored

data records include heterogeneous attributes involv-

ing different data types (e.g. location of a node, av-

erage serving time, number of processes, etc.) In

this context, users can be interested on ﬁnding fre-

quent patterns amongst these attribtues in order to

plan properly the distribution of tasks over the GRID.

The deﬁnition of ARs was ﬁrst stated in (Agrawal

et al., 1993), referring to binary attributes. Basically

it is deﬁned as follows. Let I = I

, I

, ..., I

be a set

of binary attributes, called items. Let T be a database

of transactions. Each transaction t is represented as

a binary vector, with t[k] = 1 if t bought the item

, and t[k] = 0 otherwise. Let X be a set of some

187

García López A., Berlanga R. and Danger R. (2006).

MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING.

In Proceedings of the First International Conference on Software and Data Technologies, pages 187-194

DOI: 10.5220/0001318401870194

 SciTePress

items in I. We say that a transaction t satisﬁes X

if for all items I

in X, t[k] = 1. An AR is then,

an implication of the form X ⇒ I

, where X is a

set of some items in I, and I

is a single item in I

that is not present in X. An example of this type of

rule is: ”90% of transactions that purchased bread and

butter also purchased milk”. The antecedent of this

rule consists of bread and butter and the consequent

consists of milk alone.

In (Srikant and Agrawal, 1996) where the con-

cept of Quantitative Association Rules (QARs) is ﬁrst

shown, the authors deal with the fact that the vast ma-

jority of relational databases, either based on scien-

tiﬁc or business information are not ﬁlled with binary

datatypes (as requested by the classical ARs) but with

a much richer range of datatypes both numerical and

categorical.

A ﬁrst approach to tackle this problem consists of

mapping the QARs problem into the boolean ARs

problem. The key idea is that if all attributes are cat-

egorical or the quantitative attributes have only a few

values, this mapping is straightforward. However, this

approach generates problems as if the intervals are too

large, some rules may not have the required minimum

conﬁdence and if they are too small, some rules may

not have the required minimum support. We could

also think of the strategy of considering all possible

continuous ranges over the values of the quantitative

attribute to cover the partitioned intervals (to solve the

minimum conﬁdence problem) and increase the num-

ber of intervals (solving the problem of minimum sup-

port). Unfortunately two new problems arise: First,

if a quantitative attribute has n values (or intervals),

there are on average O(n

) ranges that include a spe-

ciﬁc value or interval, fact that blows up the execution

time and second, if a value (or interval) of a quantita-

tive attribute has minimum support, so will any range

containing this value/interval, therefore, the number

of rules increases dramatically.

The approach taken by (Srikant and Agrawal,

1996) is different. Considering ranges over adjacent

values/intervals of quantitative attributes to avoid the

minimum support problem. To mitigate the problem

of the excess of execution time, they restricted the ex-

tent to which adjacent values/intervals may be com-

bined by introducing a user-speciﬁed maximum sup-

port parameter; they stop combining intervals if their

combined support exceeds this value. They introduce

as well a partial completeness measure in order to be

able to decide whether to partition a quantitative at-

tribute or not and how many partitions should there

be, in case it’s been decided to partition at all. To

address the problem of the appearance of too many

rules, they propose an interest measure based on the

deviation from the expectation that helps to prune

out the uninteresting rules (extension of the interest

measure already proposed in (Srikant and Agrawal,

1997)). Finally an algorithm to extract QARs is pre-

sented, sharing the same idea of the algorithm for

ﬁnding ARs over binary data given in (Agrawal and

Srikant, 1994) but adapting the implementation to the

computational details of how candidates are generated

and how their supports are now counted.

In (Miller and Yang, 1997), the authors pointed out

the pitfalls of the equi-depth method (interest measure

based on deviation), and presented several guiding

principles for quantitative attribute partitioning. They

apply clustering methods to determine sets of dense

values in a single attribute or over a set of attributes

that have to be treated as a whole. But although they

took distance among data into account, they did not

take the relations among other attributes into account

by clustering a quantitative attribute or a set of quan-

titative attributes alone. Based on this, (Tong et al.,

2005) improved the method to take into account the

relations amongst attributes.

Another improvement in the mining of quantitative

data is the inclusion of Fuzzy Sets to solve the sharp

boundary problem (Kuok et al., 1998). An element

belongs to a set category with a membership value,

but it can as well belong to the neighbouring ones.

In (Dong and Tjortjis, 2003) a mixed approach

based on the quantitative approach introduced by

(Srikant and Agrawal, 1996), the hash-based tech-

nique from the Direct Hashing and Pruning (DHP) al-

gorithm (Park et al., 1995) and the methodology for

generating ARs from the apriori algorithm (Agrawal

and Srikant, 1994) was proposed. The experimental

results prove that this approach precisely reﬂects the

information hidden in the datasets, and on top of it, as

the dataset increases, it scales-up linearly in terms of

processing time and memory usage.

On the other hand, the work realised by Aumann et

al. in (Aumann and Lindell, 1999), proposes a new

deﬁnition for QARs. An example of this rule would

be: sex = female ⇒ W age : mean = $7.90 p/hr

(overall mean wage = $9.02). This form of QAR,

unlike others doesn’t require the discretisation of at-

tributes with real number domains as a pre-processing

step. Instead it uses the statistical theory and data-

driven algorithms to process the data and ﬁnd regu-

larities that lead to the discovery of ARs. A step for-

ward in this kind of rules was given by (Okoniewski

et al., 2001). They provide variations of the algorithm

proposed in (Aumann and Lindell, 1999) enhancing

it by using heuristic strategies and advanced database

indexing. The whole methodology is completed with

the proposition of post-processing techniques with the

use of similarity and signiﬁcance measures.

The motivation of this work is to tackle some of the

drawbacks of the previous techniques. Most of them

require the translation of the original database so that

each non-binary attribute can be regarded as a discrete

set of binary variables over which the existing data

ICSOFT 2006 - INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES

188

mining algorithms can be applied to. This approach

can be sometimes unsatisfactory due to the follow-

ing reasons: the translated database can be larger than

the original one, the transformation of the quantita-

tive data could not correspond to the intended seman-

tics of the attributes. Moreover, current approaches do

not deal with heterogeneous attributes but deﬁne ad-

hoc solutions for particular data types (mainly numer-

ical ones). As a consequence, they do not provide a

common data mining framework where different rep-

resentations, interesting measures and value cluster-

ing techniques can be properly combined.

1.1 Overview of Our Proposal

In this article, we extend the work introduced in (Dan-

ger et al., 2004) by applying clustering techniques

in two steps of the mining process. A schematic

view of the overall process can be seen in Figure 1.

First, clustering is applied to the attribute domains,

so that each object can be expressed as a set of pairs

hattribute, clusteri instead of hattribute, valuei.

This new representation allows users to deﬁne the

most appropriate technique to discretize numeric do-

mains or to abstract categorical values. We name ob-

ject subdescription to the characterisation of an ob-

ject through value clusters. The second step consists

of clustering object subdescriptions in order to ﬁnd

frequent patterns between their features.

Figure 1: Overview of our proposal.

Finally, we propose an algorithm capable of obtain-

ing the frequent itemsets from the found object sub-

description clusters. We distinguish two kind of ARs,

namely: inter and intra-cluster. The former relate at-

tributes of different clusters, whereas the latter relate

attributes locally deﬁned in a cluster. Both kind of

ARs provide different levels of details to users, which

can mine a selected cluster involving a restricted set

of attributes (i.e. local analysis) or the whole set of

clusters (i.e. global analysis).

The paper is organised as follows: in the next sec-

tion, we introduce the necessary concepts of the pro-

posed framework. Then, in Section 3 we explain

how we include clustering in our mining process. In

Section 4 we describe a data-mining algorithm that

ﬁnds frequent object sub-descriptions, and in Section

5 we describe the preliminary experimental results.

Finally, in Section 6 we give our conclusions and we

outline the future work.

2 FORMAL DEFINITIONS

In the proposed framework, a data collection consists

of a set of objects, Ω = o

, o

, ..., o

, which are de-

scribed by a set of features R = R

, R

, ..., R

. We

will denote with D

the domain of the i-th feature,

which can be of any data type.

We will apply the clustering algorithm to the at-

tributes’ domains in order to ﬁnd groups (clusters)

of close values and use them instead of the original

values. Thus each object will be no longer charac-

terised by it’s attributes’ values but by the clusters to

which these values belong. We will denote the set of

clusters in the domain (D

) of a given attribute i as

= G

i,1

, ..., G

i,r

, being r ≥ 1 and G

i,r

the r-th

cluster in the domain of the i-th attribute.

On the other hand, we will apply a second cluster-

ing step to the object sub-descriptions in order to gen-

erate groups of objects that will help us in reducing

the ﬁnal number of rules. We will denote the set of

clusters in Ω as Θ = OG

, ..., OG

, being t ≥ 1 ≤ n

and OG

the i-th cluster in the objects’ domain.

In order to compare two attribute-clusters, each

feature R

has associated a comparison criterion,

(x, y), which indicates whether the pair of clus-

ters, x, y ∈ Π

, must be considered equal or not.

This comparison criterion can include speciﬁcations

for the case of invalid and missing values in order to

deal with incomplete information.

The simplest comparison criterion is the strict

equality, which can be applied to any domain:

C(x, y) =



1 if x = y

0 otherwise

Another interesting criteria can use the centroid of

each domain cluster. For example, being c

i,r

the cen-

troid of the r-th cluster over the i-th attribute the com-

parison function looks as follows:

If x ∈ G

a,1

and y ∈ G

a,2

then

C(x, y) =



1 if |c

a,1

− c

a−2

| ≤ ǫ

0 otherwise

Which expresses the fact that two clusters are con-

sidered equal if their centroids differ from each other

in at most a given threshold ǫ.

Since the mining process is intended to discover

MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING

189

the combinations of object features and object clusters

that frequently co-occur, it is necessary to manage the

different object projections. Thus, a subdescription of

an object o for a subset of features S ⊆ R, denoted as

(o), is the projection of o over the feature set S. In

this context, we denote o[r] the value of the object o

for the feature r.

Moreover, we assume that there exists a similarity

function between two object subdescriptions, which

allow us to decide whether two objects o

and o

must

be considered equal or not by the mining process. All

the similarity functions are binary, that is, they return

either 0 (not equal) or 1 (equal).

The simplest similarity function is the following

one:

Sim(I|

(o), I|

′

)) =

1 if ∀r ∈ S, C(o[r], o

′

[r]) = 1)

0 otherwise

which expresses the strict equality by considering

the comparison criterion of each of the subdescription

features.

Alternatively, the following similarity function

states that two subdescriptions are considered equal

if they have at least ǫ features belonging to the same

cluster:

Sim(I|

(o), I|

′

)) =

1 if |{r ∈ S|C(o[r], o

′

[r]) = 1}| ≥ ǫ

0 otherwise

In order to compare object-clusters, we can take

one representative object of each cluster. In our ap-

proach, such a representative corresponds to the ob-

ject with maximum connectivity according to the

adopted similarity function. This is because we use a

clustering algorithm that generates star-shaped clus-

ters.

Analogously to the traditional Data Mining works,

we also provide deﬁnitions of support and ARs, but

applied to this new context.

We deﬁne the support of a subdescription v = I|

(o),

denoted with Sup(v), based in the work by (Danger

et al., 2004), as the percentage of objects in Ω whose

subdescriptions are similar to v, that is:

Sup(v) =

|{o

′

∈Ω|Sim(I|

′

),v)=1}|

|Ω|

We say that a pair of subdescriptions v

= I|

(o)

and v

= I|

(o), with R

= ∅ and R

, R

⊂

R, are associated through the AR v

⇒ v

(s, c), if

Sup(v

′

) ≥ s and

Sup(v

′

)

Sup(v

)

≥ c, where v

′

= I|

∪R

(o).

The values of s and c are called support and conﬁdence

of the rule respectively.

The problem of computing the AR for complex

objects consists of ﬁnding all the AR of the subde-

scriptions of Ω whose support and conﬁdence satisfy the

user-speciﬁed thresholds.

It must be pointed out that the previous deﬁnitions

subsume the traditional concept of AR, therefore, if

we use strict equality in both the comparison crite-

rion and the similarity function, we obtain the classi-

cal deﬁnition of AR.

Besides, we can include other comparison crite-

ria such as the interval-based partitions, for quanti-

tative data, and the is-a relationship of the concept

taxonomies, in order to represent other kinds of ARs

(Srikant and Agrawal, 1997) (Z. Zhing and Zhang,

1997) (Hipp et al., 1998).

The idea that different items have different levels of

interest for the user, as suggested in (Gyenesei, 2000),

can be also incorporated in this framework by assign-

ing a weight to each variable in the similarity func-

tion. Moreover, when the variables’ data is fuzzy, it is

perfectly admissible to use as a comparison criterion

the membership of the values to the same fuzzy set.

3 FINDING INTERESTING

SUBDESCRIPTIONS

In a previous step to that of ﬁnding the interesting

ARs we will pre-process the data by means of clus-

tering algorithms in order to ﬁnd the groups that will

be the base of our mining process.

The objective of this pre-processing step is that of

identifying clusters in the domain of the attributes

that will characterise the objects we will use to ex-

tract intra-cluster rules, and identifying clusters in the

domain of the recently discovered object subdescrip-

tions in order to extract inter-cluster rules.

The algorithm chosen for this process is the Star

Clustering Algorithm introduced in (Aslam et al.,

1998), and modiﬁed to be order independent in (Gil-

Garc

ıa et al., 2003). The main reason for choosing

it is that the Star-based representation of the objects

subdescriptions seems a good way of representing the

support of each subdescription (i.e. the number of ob-

jects that are similar to it, also called, satellites, as we

will see later). Brieﬂy, the star-shaped graphs capture

the most supported subdescriptions w.r.t. the deﬁned

similarity function.

This algorithm approximates the minimal dominant

set of the β − similarity graph. The minimal domi-

nant set (Kann, 1999) is the smallest set composed

of graph’s vertexes that contains every vertex in the

graph, or at least if a vertex is not contained, it has

a neighbour that does. The members of the mini-

mal dominant set are called stars and their neighbours

satellites.

A star-shaped sub-graph of l + 1 vertexes consists

of a star an l satellites. Each sub-graph forms a group

and the stars are the objects with the biggest connec-

tivity. If an object is isolated in the graph it is consid-

ered as well a star.

The basic steps of this algorithm are the following

ones:

ICSOFT 2006 - INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES

190

• Obtain the β − similarity graph.

• Calculate the degree of every vertex.

• While there’s still ungrouped sub-vertexes do:

– Take the ungrouped vertex with the highest de-

gree.

– Build a group with it an its neighbours.

Figure 2 shows the star-shaped graph of a cluster

of object subdescriptions. The complexity of the al-

gorithm is in O(n

), being n the number of processed

objects.

Figure 2: Example of star-based object cluster.

4 EXTRACTING ASSOCIATION

RULES

In this section we present an algorithm (see Figure 3)

for computing the frequent subdescriptions for a col-

lection of complex objects. This algorithm is inspired

in the original algorithm of (Agrawal and Srikant,

1994). However, it also uses the strategy of the Par-

tition algorithm (Srikant and Agrawal, 1997) to com-

pute the support of object subdescriptions.

It is worth mentioning that in this work an item-

set is a subdescription, and its support is the number of

objects in the database that are similar to it.

The algorithm works as follows: ﬁrst, it determines

all the groups of values for each feature by using

the SetFreqClusters function, which applies the speciﬁc

clustering criterion and similarity function deﬁned for

it. Then, while at least two groups have been found in

the previous iteration k, they are combined two by two

in order to create candidate sets of k + 1 features. Af-

terward, it determines, for each combination of vari-

ables, which of the candidate subdescriptions are fre-

quent enough.

It’s important to take into account that in order to

guarantee the monotonic construction of the frequent

itemsets, it is necessary that the similarity functions

satisfy the folowing condition: if two objects are dif-

ferent with respect to a subdescription S

, they are

Table 1: Features of the “Flags of the World” database.

Example database

Feature Number Feature name Domain

1 Colours Set of Colours

2 Religion Religions

3 Number of Colours Integer

4 Continent Continents

5 Number of vertical bars Integer

6 Number of horizontal

stripes

Integer

7 Number of sun or star

symbols

Integer

8 Number of circles Integer

9 Predominant colour Set of Colours

10 Colour in the top-left cor-

ner

Set of Colours

11 Colour in the bottom-left

corner

Set of Colours

12 Geographic quadrant NE, SE, SW, NW

yellow, gold, red, green, blue, brown, orange, white,

black

Catholic, Other Christian, Muslim, Buddhist, Hindu,

Ethnic, Marxist, Others

North America, South America, Europe, Africa,

Asia, Oceania

also different with respect to any other subdescription

, such that S

⊂ S

(Danger et al., 2004).

5 PRELIMINARY RESULTS

In this section we will give an example of the type of

ARs that are extracted from a database once applied

the mining process.

As earlier mentioned, we apply the mining algo-

rithm in two ways, intra- and inter-cluster. In order to

give examples of this kind of rules, we have taken the

well-known “Flags of the world” database

, which is

summarized in Table 1.

We will use the same notation as in the formal

framework for the different clusters, being for exam-

ple, G

1,2

, 2nd cluster in the domain of the 1st variable

and OG

the 2nd cluster of object subdescriptions.

5.1 Intra-cluster Rules

Let us suppose that we deﬁne the following clusters

for the different feature domains:

• Colour (colors in the ﬂag): G

1,1

= (white, red),

1,2

= (blue), G

1,3

= (green).

http://www.cia.gov/cia/publications/factbook/docs

MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING

191

Figure 3: Data Mining Algorithm.

Table 2: Object clusters found by the Star-based clustering

algorithm.

Object Clusters

Object Cluster SUBDESCRIPTIONS #OBJ

1,1

, G

2,1

), (G

1,1

, G

2,2

) 20

1,3

, G

2,3

), (G

1,2

, G

2,3

) 10

3,2

, G

1,1

), (G

3,2

, G

1,2

) 15

3,1

, G

1,3

), (G

3,1

, G

1,2

) 30

4,4

, G

1,3

), (G

3,1

, G

1,3

3,2

, G

1,3

)

3,2

, G

1,1

, G

6,1

, G

5,1

3,2

, G

1,2

, G

6,1

, G

5,1

)

• Religion (majority religion in the country): G

2,1

(Catholic), G

2,2

= (OtherChristian), G

2,3

(Other).

• Number of Colours (number of different colors

present in the ﬂag): G

3,1

= (2), G

3,2

= (3, 4).

• Continent (Continent to which the country be-

longs): G

4,1

=”North America”, G

4,2

=”South

America”, G

4,3

=”Europe”, G

4,4

=”Africa”,

4,5

=”Asia”, G

4,6

=”Oceania”.

• Number of vertical bars: G

5,1

= (1), G

5,2

= (2),

5,3

= (3), G

5,4

= {i|i > 3}.

• Number of horizontal stripes: G

6,1

= (1), G

6,2

(2), G

6,3

= (3), G

6,4

= {i|i > 3}.

• Number of star and sun symbols: G

7,1

= (1),

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192

7,2

= (2), G

7,3

= (4), G

7,4

= (5), G

7,5

= {i|i >

5}.

• Number of circles: G

7,1

= 1, G

7,2

= 2, G

7,3

= 3,

7,4

= 4, G

7,5

= {i|i > 4}.

• Predominant color: G

9,1

=”yellow”, G

9,1

=”gold”,

9,1

=”red”, G

9,1

=”green”, G

9,1

=”blue”,

9,1

=”brown”, G

9,1

=”orange”, G

9,1

=”white”,

9,1

=”black”.

• Colour in the top-left corner: G

10,1

=”yellow”,

10,1

=”gold”, G

10,1

=”red”, G

10,1

=”green”,

10,1

=”blue”, G

10,1

=”brown”, G

10,1

=”orange”,

10,1

=”white”, G

10,1

=”black”.

• Colour in the bottom-left corner: G

11,1

=”yellow”,

11,1

=”gold”, G

11,1

=”red”, G

11,1

=”green”,

11,1

=”blue”, G

11,1

=”brown”, G

11,1

=”orange”,

11,1

=”white”, G

11,1

=”black”.

• Geographic quadrant: G

12,1

=”NE”, G

12,2

=”SE”,

12,3

=”SW”, G

12,4

=”NW”.

We obtain the following rules from two of the de-

tected subdescription clusters (see Table 2):

• From OG

: Number of Colours = G

3,2

, Col our =

1,1

(31%, 80%). Meaning that in the 80% of the

cases, if a ﬂag contains 3 or 4 different colors, one

of them is either red or white. This rule has a sup-

port of 31%.

• From OG

: Religion = G

2,2

, Colour =

1,1

(21%, 68%). Meaning that in the 68% of the

cases, if a country’s majoritary religion is the Chris-

tian (other than the Roman Catholic), its ﬂag con-

tains red or white. This rule has a support of 21%.

• From OG

: Colour = G

1,2

, Colour =

1,1

(32%, 63%). The color blue implies the

presence of both colors red and white in the 63%

of cases.

• From OG

: Number of Colours = G

3,2

Horizontal Stripes = G

6,1

, V erticalBars =

5,1

(20%44%). In 44% of the cases a ﬂag contain-

ing 3 or 4 colors is composed by one quadratic

section.

5.2 Inter-cluster Rules

Inter-cluster rules involve object clusters that satisfy

the following conditions:

• the intersection between their member sets is

greater than the minimum support value MinSupp

and

• they do not share some of their features.

For example, from Table 2, the follow-

ing pairs are candidate to be mined for

ﬁnding inter-cluster rules: (OG

, OG

(OG

, OG

),(OG

, OG

),(OG

, OG

),(OG

, OG

)

and (OG

, OG

). For each of these pairs, the mining

algorithm calculates all the frequent object subde-

scriptions. For example, the following rules have

been extracted from the previous cluster pairs:

• From (OG

, OG

): In 30% of the cases, the coun-

tries where the majoritary religion is the Christian,

their ﬂags have the colors White, Red or Blue.

• From (OG

, OG

): In 40% of the cases, countries

in which the majoritary religion is other than the

Christian,their ﬂags have less than 3 colors, being

one of them either blue or green.

• From (OG

, OG

): In 30% of the cases, the African

countries have a religion different from the Chris-

tian.

• From (OG

, OG

), In 35% of the cases, the ﬂags

with just one quadratic section contain Blue, Red

and White.

Notice that the same mining algorithm of Figure

3 is applied to ﬁnd both inter- and intra-cluster as-

sociation rules. The difference consists of the set of

subdescription objects that is used as input. For local

analysis, just the members of a single object cluster is

passed to the algorithm. Instead, for global analysis,

the union of the members of a set of related candidate

clusters is passed to the algorithm.

6 CONCLUSIONS AND FUTURE

WORK

This paper presents a general framework for mining

complex objects represented with any of the existing

data models (e.g. relational, object-oriented and

semi-structured data models). The mining process

is guided by the semantics associated to each object

description feature (attributes), which are stated by

the users by selecting the appropriate representation

model. This was the model introduced by (Dan-

ger et al., 2004). Furthermore, we have extended

the framework to enrich the formal representation

of the objects using clusters of both attributes

and objects, so that the mining process results in

an acceptable number of higher level rules. We

show as well examples of this semantically richer

rules. The future work includes carrying out a

series of experiments over well-known databases

(e.g ftp://ftp.ics.uci.edu/pub/machine-learning-

databases/) and the Monalisa repository database

(http://alimonitor.cern.ch:8889), which is the Grid

monitoring database for the ALICE experiment at

CERN, in order to prove that the proposed method is

generating the expected results.

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193

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