MINING THE COSTA RICAN WEB

Esteban Meneses

Computing Research Center - Costa Rica Institute of Technology

P.O.Box 159-7050, Cartago, Costa Rica

Keywords:

Web mining, local web, web clustering.

Abstract:

There is much to say about the structure and composition of a local web. Identiﬁcation of authorities, topics

and web communities can be used to improve search engines, change a portal design or to develop marketing

strategies. The Costa Rican web was chosen as a test case for web mining analysis. After the study we

obtained several descriptors of the web as well as the answers to typical questions like how many pages on

average a site has, which ﬁle type is preferred for building a dynamic site, what is the most referenced site,

which sites are similar, and many more.

1 INTRODUCTION

The World Wide Web is formed by an unusual and het-

erogeneous set of elements. This property makes the

web an important and ﬂexible mechanism for commu-

nication and information storage, but makes its analy-

sis hardly easy. Luckily, many recent research efforts

have been focused on how to overcome this challenge

and provide a set of descriptors from a local web.

This paper presents the results after the analysis of

the Costa Rican web. This subset of the global web is

formed by every page in a site on the .cr domain.

Many interesting facts can be discovered from this

collection. Things like what is the most referenced

site, what is the site with more outgoing links, which

sites compose the core of the web, how old are web

pages, what is the most common ﬁle type for dynamic

pages, which site is similar to some given web site,

and many more, can be revealed by applying several

web mining techniques.

When studying a collection of web pages, it is

fundamental to analyze it in two dimensions: web

structure and web composition. The former provides

the intricate form on which web pages and sites are

linked, while the latter offers some description about

the topics covered in the web pages or sites. Also, the

HTTP protocol generates lots of information with ev-

ery web transaction. All these statistics are relevant to

describe the collection in a physical sense.

The data obtained from the analysis can be ap-

plied to a variety of processes: construction of web

crawlers, improving web browsing and searching, de-

sign of web portals, optimization for web servers, fo-

cusing a marketing strategy, among others.

The results presented in this paper were generated

using a couple of tools. The Wire set of tools (WIRE)

for web mining and analysis and Efﬁmining (PRE-

DISOFT) for web site clustering. Also, it was nec-

essary to develop miscellaneous tools for extracting

information from web pages.

However, in the Kl

a research project at the Com-

puting Research Center of the Costa Rica Institute of

Technology, we are currently developing several tools

for web mining in the MS .net platform which are

focused on mining a local web. The ﬁnal objective is

to construct a portal for web searching and web col-

location analysis.

Section 2 introduces the topic of web mining and

many core concepts related with the statistical de-

scriptors of a local web. After that, section 3 presents

the results of the analysis made on the Costa Rican

web. These statistics can serve as an example of what

can be known from a subset of the global web. Sec-

tion 4 presents some experiments done when cluster-

ing some of the Costa Rican web sites. Here, the inter-

est will be focused in the representation used as well

as the different strategies for grouping the elements

of a collection. Finally, section 5 offers some conclu-

sions and future work.

414

Meneses E. (2006).

MINING THE COSTA RICAN WEB.

In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Internet Technology / Web

Interface and Applications, pages 414-421

DOI: 10.5220/0001249504140421

 SciTePress

2 WEB MINING

This section surveys the basic principles on which

web mining is based. These fundamentals come

from many disciplines: data structures, information

retrieval, formal languages, parsing theory, statistics

and many more.

The analysis of a web page collection usually im-

plies at least two dimensions: structure and compo-

sition. The ﬁrst refers to how pages are linked and

that gives a means for authority. The latter is related

with the content of a page. These two dimensions are

orthogonal and both are usually taken into account in

any analysis.

2.1 Structure

The web or a fraction of it can be seen as a big di-

rected graph. If every page is considered as a node

and a hyperlink from a url to another is considered

as a link, then every collection of web pages is a di-

rected graph, not necessarily connected. However, if

sites are considered as nodes, then the same is true.

As a graph, all the theory around it can be ap-

plied. Then, shortest paths can be found from a url

to another, cliques can be discovered, cycles found,

strongly connected components detected, you name

it.

Figure 1: Macrostructure of the Web.

Figure 1 shows the macroscopic structure of the

web (Castillo, 2004). It is important to deﬁne the fol-

lowing components:

• MAIN: is the set of the biggest strongly connected

component in the web graph. It can be divided into

MAIN-IN if sites are linked from IN, MAIN-OUT

if sites link to OUT. MAIN-MAIN is the intersec-

tion of both sets and MAIN-NORM is the rest of

set MAIN.

• IN: is the set of all sites not in MAIN that link some

site in MAIN.

• OUT: is the set of all sites not in MAIN being

linked from MAIN.

• TUNNEL: is the set of all sites linked from IN and

that link to OUT.

• TENTACLES: sites linked from IN or that link to

OUT, but not both. Tentacles-IN is the set with sites

that follow the ﬁrst condition while Tentacles-Out

is the set of sites following the second condition.

• ISLANDS: is the set of sites that neither are linked

from IN nor link to OUT.

This characterization of web sites is important

since it can give a measure of how important and old

a site is. The most important part of a web is in set

MAIN, it is the core of the web. Sites in IN are most

likely new sites linking to big and important sites.

Sites in OUT are typically old sites without mainte-

nance. It is clear that sites in ISLANDS are probably

not important because are separated from the rest of

the graph.

Continuing with the graph structure, the degree of

a node is calculated by the number of links this node

has. The in-degree is the number of incident links

(reach the node) and the out-degree is the number of

links going out the node. The ﬁrst value is usually im-

possible to calculate, because it can not be established

how many people link some site. The second value is

easy to obtain given a parsing of the HTML code.

The granularity of all this analysis can be selected

between sites and pages. The structure of links from

web pages form a graph where the pages are nodes

and there is a link between two pages if there is a hy-

perlink in the HTML code. Analogously, the structure

of web sites can also be a graph if nodes are consid-

ered as sites and there is a link between two sites if

there is a page in one site linking a page on other site.

In this paper both approaches were taken, but it will

be clear which one is used in each descriptor.

The number of incoming links and outgoing links

in a web collection typically follows a power law.In

this case the probability that a page has value x for

some of the outlined characteristics is directly propor-

tional to:

Where k is the parameter of the distribution.

2.2 Composition

A web collection can be also analyzed according the

content of their documents. This approach follows a

typical information retrieval style where documents

are represented in a multidimensional space. Thus,

MINING THE COSTA RICAN WEB

415

every document is seen as a vector, where entries cor-

respond to terms. The most frequently used model is

the TF-IDF.

TF stands for the term frequency and it is simply the

number of times term t occurs in document d. It can

be scaled in a variety of ways to normalize document

length and to obtain TF(d, t).

IDF is the inverse document frequency. Not all axes

are equally important. Coordinates corresponding to

function words like a, an and the will be large and

noisy irrespective to the content of the document. IDF

tries to scale down the coordinates of terms that occur

in many documents. There are also many ways to cal-

culate IDF (t) (Chakrabarti, 2003).

Finally, the model joints each of the last computa-

tions in some way, a classic formula is:

= TF(d, t )IDF (t)

Where d

is the coordinate of document d in dimen-

sion t.

Nevertheless, there is a couple of important pre-

processes made in the composition analysis. The ﬁrst

one is the elimination of common words that add lit-

tle to the semantic of the document. They are called

stopwords and many prepositions fall in this set. After

stopword removal a typical action used is stemming,

which consist in transforming every word to its lex-

ical root or stem. For example, all following words

{apply, applying, applies} will fall in the

stem appl.

3 THE COSTA RICAN WEB

Costa Rica is one of the seven countries that form

Central America. It has a population around 4.5 mil-

lion people and according to UNCTAD it is the sec-

ond country in Latin America where most people use

Internet (Chile is the ﬁrst). Given that, is sounds in-

teresting to study and characterize the web in Costa

Rica. This work was inspired by a study made by our

Chilean colleagues (Baeza-Yates, 2003).

For measuring and extracting all the required in-

formation about the Costa Rican web, the Wire set

of tools (WIRE) was used. WIRE is an open source

crawler, repository and analyzer focused on schedul-

ing techniques for crawling (Castillo, 2004). Wire

was run on a Pentium 4 machine with 256 Mb RAM

and 256 Gb on hard disc. The total crawling took ap-

proximately 4 days.

In this section, the results Wire found for the doc-

uments and sites will be presented . It will include

characteristics as size, age, structure and languages.

The search space was delimited to the Costa Rican

web, deﬁned by all documents within the .cr do-

main (other deﬁnitions of Costa Rican web are also

possible). For extracting all site names ending in .cr

several sources were crawled: Netcraft (NETCRAFT)

which is a site with lots of information about web

servers around the net, ODP (ODP) the Open Direc-

tory Project which lists many interesting pages and

Google search engine. All this should be done given

that the local NIC (CRNIC) was unable to publish the

complete site list due to security reasons.

3.1 Documents

The ﬁrst thing to note is that the total crawled pages

were near seven hundred thousand, which is smaller

if it is compared with the 3 million pages Chilean web

(Castillo, 2004). Table 1 shows a summary of pages

in the Costa Rican web. It can be seen that there are

few duplicates among pages and that Costa Rican web

is mostly static.

Table 1: Summary of Costa Rican web pages.

Total pages 731,171 100%

Unique 711,504 97.31%

Duplicates 19,667 2.69%

Static 542,247 74.16%

Dynamic 188,924 25.84%

Also, it is a web composed by mostly new docu-

ments. Almost one of every ﬁve pages has less than

12 months, but this stands for half the pages whose

age was known (some web server doesn’t return the

age of document). Figure 2 shows the distribution of

age for pages.

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 1 2 3 4 5

Fraction of pages

Document age in Years

Figure 2: Age of pages.

These results are consistent with the ones found in

(Castillo, 2004) from the Chilean web. Another ex-

pected result was the number of incoming links, as

shown in ﬁgure 3.

The number of incoming links is calculated within

the collection, counting how many hyperlinks point to

WEBIST 2006 - WEB INTERFACES AND APPLICATIONS

416

some page. This measure is important as a means for

authority. However, as it was mentioned in section 2 it

follows a power law, meaning that the probability for

a page to have an incoming degree equal to x is pro-

portional to

1.66

. Figure 3 presents this result. The

value 1.66 is exactly the same to its analogous value

for Chilean web. As this value gets smaller, it is easier

to ﬁnd a page with higher incoming degree. The less

this parameter, the more biased the distribution is.

1e-09

1e-08

1e-07

1e-06

1e-05

1e-04

0.001

0.01

0.1

1 10 100 1000 10000 100000

Fraction of pages

Number of incoming links

k/x^1.66 in [10,1000]

Figure 3: Incoming links.

On the other side, the PageRank measure (Page,

1998) discriminates pages according to their author-

ity. It is a measure of how important a page is. Fig-

ure 4 shows the power distribution for pages accord-

ing to their PageRank. In this case the parameter is

a standard one, 1.85. This result is a little bit bi-

ased, but represents the reality in the web: few pages

are very authoritative. Another measure for authority

in the web is the one called HITS (Kleinberg, 1999).

This algorithm classify pages into two groups: hubs

which references other important pages and authori-

ties which are referenced by many other pages. How-

ever, HITS creates a fuzzy classiﬁcation of pages be-

tween these two categories. Thus, every page has its

hub score and its authority score. The value for the

power distribution of the hub score is 1.87, while

the one for the authority score is 1.85. These values

of parameters were also expected.

Besides all those measures for structure, a sim-

ple but interesting counting measure can determine

which technology for cgi pages is dominant. Figure

5 presents a chart with all these technologies accord-

ing to their percent of presence in the Costa Rican

web. As it can be seen PHP dominates the construc-

tion for dynamic pages, followed by ASP and then for

SHTML.

Another statistic regarding the language of the doc-

uments is that English is dominant over Spanish in the

web pages. In a sample taken by WIRE, 72.58% of

web pages were in English, while only 26.51% were

in Spanish.

1e-12

1e-10

1e-08

1e-06

1e-04

0.01

1e-07 1e-06 1e-05 1e-04 0.001 0.01 0.1

Fraction of pages

Page rank

k/x^1.85 in [5e-6,5e-5]

Figure 4: PageRank.

Figure 5: Extensions cgi.

3.2 Sites

In the Costa Rican web every domain must be col-

located into one of the following categories: .co or

commercial, .or or organizational, .fi of ﬁnance,

.ac or academic, .go or governmental, .ed or edu-

cational and .sa or health. The ﬁrst level domain is

restricted, few sites lay into such privileged domain.

According to Costa Rica NIC (CRNIC) there are

around 5000 sites in all categories. The initial list

for crawling contain less than 3000 sites. However

it doesn’t seem to be a problem, given that half the

sites are working and from the ones working half have

more than one page (Castillo, 2004).

As it was reviewed in section 2 the web can be

thought as a directed graph. Graphical visualizations

are always good to see. Figure 6 shows the graph for

all sites present in the Costa Rican web crawled by

Wire. Every point stands for a site and the most ref-

erenced sites are located in the center of the ﬁgure.

Watching this representation it is easy to get an idea

of how intricated the web structure is.

A more clear representation is found in ﬁgure 7

MINING THE COSTA RICAN WEB

417

Figure 6: All sites graph of Costa Rican web.

where the minimum spanning tree has been drawn.

The center for any star correspond to a very impor-

tant site which is linked to many others. This can be

because its authority or hub condition. For example,

the center of the big star correspond to the Univer-

sity of Costa Rica (which will be mentioned below).

These two ﬁgures were generated using LGL software

(LGL).

Besides the structure of web, there are more tacit

things to observe. A few basic statistics are shown in

table 2. The total number of sites ”alive” is 2,152.

The average number of pages per site is more than

ﬁve hundred, which means sites are big, but not so

complex given that the depth average is less than four

levels. Also, average site is not big in size, given that

less than 5MB are enough to contain it all.

Table 2: Site summary.

Number of sites ok 2,152

Average internal links 3,957.65

Number of sites with valid page age 1,682

Average pages per site 514.96

Average static pages per site 325.85

Average dynamic pages per site 189.11

Average of age of oldest page in months 18.91

Average of age of average page in months 12.83

Average of age of newest page in months 10.12

Average site size in MB 5.53

Average site max depth 3.12

Average in-degree 2.02

Average out-degree 2.02

Figure 7: All sites minimum spanning tree of Costa Rican

web.

Table 3: Top 5 sites according to number of documents.

Top sites by count doc count doc

www.fcj.or.cr 100,000

www.ecyt.ac.cr 100,000

ftp.ucr.ac.cr 100,000

www.emate.ucr.ac.cr 88,313

www.ulasalle.ac.cr 76,031

Following those statistics some questions arise al-

most immediately, like what is the ranking of sites?.

It depends on what it is considered. Tables 3, 4 and 5

presents three rankings according to number of docu-

ments, incoming degree and outgoing degree, respec-

tively.

The ﬁrst three places of table 3 have 100,000 doc-

uments because this was the maximum documents

crawled by site. The ﬁrst two places in this table are

sites with an online calendar technology for showing

events for every day of year. This software creates

an html for every day and then the number of web

pages counted are huge, reaching easily the limit of

web pages crawled for a site. The third site is an im-

mense site with thousands of documents, located into

the University of Costa Rica. The fourth place is also

located on the University of Costa Rica and contains

big online manuals for doing math typesetting.

In table 4 the ﬁrst three places are national uni-

versities of Costa Rica, which tend to be authorita-

tive enough according to (Castillo, 2004). The very

ﬁrst place correspond to the University of Costa Rica,

WEBIST 2006 - WEB INTERFACES AND APPLICATIONS

418

Table 4: Top 5 sites according to in degree.

Top sites by in degree in degree

www.ucr.ac.cr 138

www.una.ac.cr 84

www.itcr.ac.cr 62

cariari.ucr.ac.cr 55

www.inbio.ac.cr 53

Table 5: Top 5 sites according to out degree.

Top sites by out degree out degree

www.infoweb.co.cr 165

www.directorio.co.cr 156

sibdi.bldt.ucr.ac.cr 126

www.asamblea.go.cr 117

www.racsa.co.cr 102

which is the oldest and biggest university in Costa

Rica, formed by many faculties and research centers.

The second and third places are also public univer-

sities, which group several departments. The ﬁfth

place is the National Biodiversity Institute, which is

very important for Costa Rica, a country in the top 20

countries with more biodiversity in the world.

In the other hand, web directories, like the ﬁrst

two places in table 5 act as hubs in the web. The

third place in this table represents an instance of the

government of Costa Rica that link many other de-

partments. The ﬁfth place correspond to RACSA,

which centrally administers the internet service in

Costa Rica.

The same as PageRank is calculated for documents,

SiteRank can be calculated for sites. Figure 8 shows

the distribution for sites according to their SiteRank.

The parameter for the power law is 1.81 which again

represents an expected parameter.

1e-16

1e-14

1e-12

1e-10

1e-08

1e-06

1e-04

0.01

1e-07 1e-06 1e-05 1e-04 0.001 0.01 0.1

Fraction of sites

Siterank

k/x^1.81 in [1e-6,1e-4]

Figure 8: SiteRank.

Recalling what was discussed in section 2 regard-

ing location of sites in the graph, table 6 presents

some measures for each component. It can be seen

that the principal component has 336 sites, which

represents almost the 15% of total sites alive. Also, it

is interesting to note that the IN component has few

sites, but the OUT component has many sites for a

suspicious big old web section. Also, the ISLAND

size is huge, meaning that a big portion of the web is

disconnected from the core.

Table 6: Graph component sizes.

Component name Number of sites Percent

MAIN NORM 107 4.97%

MAIN MAIN 84 3.90%

MAIN IN 36 1.67%

MAIN OUT 109 5.07%

IN 100 4.65%

OUT 388 18.03%

TIN 22 1.02%

TOUT 28 1.30%

ISLAND 1,278 59.39%

Last but not least, it can be analyzed which are the

domains that are more commonly referenced by sites

in the Costa Rican web. Figure 9 shows the top do-

mains referenced from sites in Costa Rica. The .cr

domain is the most referenced because is the same

domain. Domain .com is the second one and Ar-

gentina (.ar) domain is the third one, followed by

Brazil (.br).

Figure 9: Top level domains.

4 WEB CLUSTERING

This section discusses the alternatives for clustering

web objects and presents a method for clustering sites

MINING THE COSTA RICAN WEB

419

using the classic vectorial representation. Some visu-

alizations of results are also showed.

4.1 Approaches

The utility of clustering for text and hypertext in-

formation retrieval relies on the cluster hypothesis,

which states that given a suitable clustering of a col-

lection, if the user is interested in document d,heis

likely to be interested in other members of the cluster

to which d belongs to (Chakrabarti, 2003). There are

many alternatives for clustering web objects: pages or

sites (Chakrabarti, 2003; Jain, 1999).

The Hierarchical Agglomerative Clustering (HAC)

starts with all the documents and successively com-

bine them into groups within which interdocument

similarity is high, collapsing down as many groups

as desired. This kind of clustering is called bottom-up

or agglomerative.

On the other side, a very well known top-down

method is k-Means, where the user preset a number

k of desired clusters. The initial conﬁguration is arbi-

trary, consisting of a grouping of the documents into

k groups and k centroids computed accordingly. The

basic step is moving to the nearest centroid for every

document. Then centroids are again calculated and

the algorithm follows until no change is made on the

assignment of document to clusters.

Although HAC and k-Means are very simple and

fast, many other algorithms have been presented: self

organized maps, multidimensional scaling, latent se-

mantic indexing, probabilistic approaches, through

summarization (Shen, 2004), hyperlink-based (He,

2001), partitioning-based (Boley, 1999) and many

more.

However, all these techniques were developed un-

der the scalar vector space model. Here, documents

are represented as vector in multidimensional Eu-

clidean space. Each axis in this space corresponds

to a term (token). Such representation makes possible

the TF-IDF model.

Apart from these techniques based on composition,

there are other techniques for web clustering based on

structure, which applies graph theory for building the

clusters (He, 2001).

4.2 Web Site Clustering

This section provides the results for clustering web

sites using a standard vectorial representation, as the

one proposed in section 2 for TF-IDF model. We se-

lected 16 sites from the top 20 sites according to their

SiteRank. This set contains scientiﬁc sites, business

sites, universities sites and one sport site. For each

site, 10 random web pages were crawled and from

these 10 pages a vector is obtained for representing

each site.

Using the 160 crawled pages, the 200 most com-

mon tokens were identiﬁed, after stopword elimina-

tion and stemming application. Then, a normalized

200 long vector is associated for each site. This nor-

malization doesn’t take into account the IDF part of

the TF-IDF model. With this representation we ap-

plied the HAC procedure, discussed in the previous

section.

Nevertheless, for visualization purposes, a PCA

(Principal Component Analysis, a dimension reduc-

tion technique) was applied to our web site database.

Figure 10 shows the result after a discrete PCA (Bun-

tine, 2004) is applied. This 2D graphic shows how

web sites are distributed and it can be seen that on the

upper right corner are located the universities and sci-

entiﬁc sites, while the business sies are on the bottom

right corner.

Figure 10: Principal component analysis for web sites.

Figure 11 presents the dendogram resultant from

the HAC procedure. Again, the scientiﬁc sites are lo-

cated in the bottom branch on the tree, while the busi-

ness sites are located in the middle branch.

Figure 11: Hierarchical agglomerative web site clustering.

Both ﬁgures were generated using Efﬁmining prod-

uct of Predisoft (PREDISOFT).

WEBIST 2006 - WEB INTERFACES AND APPLICATIONS

420

5 CONCLUSION AND FUTURE

WORK

This work is part of the effort done in the Kl

a research

project, which is under development in the Comput-

ing Research Center at the Costa Rica Institute of

Technology. Its website is www.kla.ic-itcr.ac.cr.

a is focused on characterize the Costa Rican web,

based on its structure, composition and clustering. All

this knowledge can be applied to develop several tools

for improving the web experience. A new search en-

gine has been developed under the MS .net tech-

nologies. It will be available soon at www.kla.ac.cr.

In this paper it has been showed that Costa Rican

web has around seven hundred thousand documents.

Most of them are static and relatively new. Almost

82% of documents are html text. Distributions of in-

coming links, outgoing links, pagerank, hubrank and

authorityrank follow expected power laws.

It was also presented a clustering of 16 sites of

Costa Rican web according to their popularity. This

technique builds a classic vector for each site and then

applies a HAC or k-means for clustering. Currently,

we are working on a symbolic representation for web

sites, using histograms (Rodr

ıguez-Rojas, 2000) for

measuring the frequency of terms in different axis of

a web document: text, title, links and bold. Symbolic

representations of web objects are not new, Schenker

et al have been applied a graph representation for clus-

tering web documents (Bunke, 2003).

ACKNOWLEDGEMENTS

The author would like to thank several people that

help in the construction of this paper: Dr. Oldemar

Rodr

ıguez-Rojas for his guidance in the very ﬁrst ex-

periments on symbolic web clustering, Dr. Carlos

Castillo for his help using WIRE system and Dr. Fran-

cisco J. Torres-Rojas for his support in the establish-

ment of the Kl

a research project.

REFERENCES

Baeza-Yates R., Poblete B. and Saint-Jean, F. (2003). Evo-

lution of the Chilean Web: 2001-2002 (In Spanish).

In Proceedings of the Jornadas Chilenas de Com-

putaci

on. Chill

an, Chile, November 2003.

Boley D., Gini M., Gross R., Han S., Hastings K., Karypis

G., Kumar V., Moore J. and Mobasher B. (1999).

Partitioning-Based Clustering for Web Document Cat-

egorization. In Decision Support Systems. 1999.

Bunke H., Last M., Schenker A. and Kandel A.(2003).

A comparison of two novel algorithms for clustering

web documents. In Proceedings of the 2nd Interna-

tional Workshop on Web Document Analysis (WDA).

2003.

Buntine W., Perttu, S. and Tuulos V. (2004). Using Dis-

crete PCA on Web Pages. In ECML/PKDD 2004, Pro-

ceedings of the Workshop on Statistical Approaches

for Web Mining. Pisa, Italy, September, 2004.

Castillo C. (2004). Effective Web Crawling. PhD Thesis,

University of Chile, 2004.

Chakrabarti, S. (2003). Mining the Web. Morgan Kaufmann

Publishers, 2003.

COSTA RICA NIC: Domain name services for Costa Rica.

http://www.nic.cr.

He X., Zha H., Ding C. and Simon H. (2001). Web Doc-

ument Clustering Using Hyperlink Structures. Tech-

nical Report CSE-01-006, Dept. of Computer Science

and Engineering, Pennsylvania State University, 2001.

Jain A.K., Murty M.N. and Flynn P.J. (1999). Data Cluster-

ing: A Review In ACM Computing Surveys. Septem-

ber, 1999.

Kleinberg J. (1999). Authoritative sources in a hyperlinked

environment. In Journal of the ACM. 46(5):604632,

1999.

LGL: Large Graph Layout

http://apropos.icmb.utexas.edu/lgl/

NETCRAFT. http://news.netcraft.com/.

ODP: Open Directory Project. http://dmoz.org.

Page L., Brin S., Motwani R. and Winograd T. (1998). The

PageRank citation ranking: bringing order to the Web.

Technical report, Stanford Digital Library Technolo-

gies Project, 1998.

Predisoft: Scientiﬁc Prediction. http://www.predisoft.com.

Rodr

ıguez-Rojas O. (2000). Classiﬁcation and Linear Mod-

els in Symbolic Data Analysis. PhD Thesis, Univer-

sity of Paris IX-Dauphine, 2000.

Shen D., Chen Z., Yang Q., Zeng H., Zhang B., Lu Y.

and Ma W. (2004). Web-page Classiﬁcation through

Summarization. In SIGIR 2004, Proceedings of the

ACM Conference on Research & Development on In-

formation Retrieval. South Yorkshire, United King-

dom, July, 2004.

WIRE: Web Information Retrieval Environment.

http://www.cwr.cl/projects/WIRE/.

MINING THE COSTA RICAN WEB

421