Content uploaded by Jonas Poelmans
Author content
All content in this area was uploaded by Jonas Poelmans on Feb 27, 2015
Content may be subject to copyright.
A concept discovery approach for fighting human
trafficking and forced prostitution
Jonas Poelmans1, Paul Elzinga3, Guido Dedene1,4, Stijn Viaene1,2, Sergei
Kuznetsov5
1K.U.Leuven, Faculty of Business and Economics, Naamsestraat 69,
3000 Leuven, Belgium
2Vlerick Leuven Gent Management School, Vlamingenstraat 83,
3000 Leuven, Belgium
3Amsterdam-Amstelland Police, James Wattstraat 84,
1000 CG Amsterdam, The Netherlands
4Universiteit van Amsterdam Business School, Roetersstraat 11
1018 WB Amsterdam, The Netherlands
5State University Higher School of Economics (HSE), Moscow, Russia
{Jonas.Poelmans, Stijn.Viaene, Guido.Dedene}@econ.kuleuven.be
skuznetsov@hse.ru
Paul.Elzinga@amsterdam.politie.nl
Abstract. Since the fall of the Iron curtain starting in 1989 in Hungary, millions
of Central and Eastern European girls and women have been forced to work in
the European sex industry (estimated 175,000 to 200,000 yearly1). In this paper,
we present our work with the Amsterdam-Amstelland (Netherlands) police to
find suspects and victims of human trafficking and forced prostitution. 266,157
suspicious activity reports were filed by police officers between 2005 and 2009
that contain their observations made during a police patrol, motor vehicle
inspection, etc. We used FCA to filter out interesting persons for further
investigation and used the temporal variant of FCA to create a visual profile of
these persons, their evolution over time and their social environment. We
exposed multiple cases of forced prostitution where sufficient indications were
available to obtain the permission from the Public Prosecutor to use special
investigation techniques. This resulted in a confirmation of their involvement in
human trafficking and forced prostitution resulting in actual arrestments being
made.
1. Introduction
Irina, aged 18, responded to an advertisement in a Kiev, Ukraine newspaper for a
training course in Berlin in 1996. With a fake passport, she traveled to Berlin,
Germany where she was told that the school had closed. She was sent on to Brussels,
Belgium for a job. When she arrived she was told she needed to repay a debt of
1 Eerste rapportage Nationaal Rapporteur Mensenhandel
http://www.bnrm.nl/Images/Rapportage%201%20(Ned)_2002_tcm63-83113.pdf
US$10000 and would have to earn the money in prostitution. Her passport was
confiscated, and she was threatened, beated and raped. When she didn't earn enough
money, she was sold to a Belgian pimp who operated in Rue D'Aarschot in the
Brussels red light district. When she managed to escape through the assistance of
police, she was arrested because she had no legal documentation. A medical exam
verified the abuse she had suffered, such as cigarette burns all over her body (Hughes
et al. 2003).
The above story is a typical example of a woman of Eastern Europe who was
forced into the European sex industry. Rough estimates suggest that 700,000 to 2
million women and girls are trafficked across international borders every year
(O’Neill 1999, U.S. Department 2008). The majority of transnational victims are
trafficked into commercial sexual exploitation. Human trafficking is the fastest
growing criminal industry in the world, with the total annual revenue for trafficking in
persons estimated to be between $5 billion and $9 billion (United Nations 2004). The
council of Europe states that “people trafficking has reached epidemic proportions
over the past decade, with a global annual market of about $42.5 billion” (Equality
division 2006). The most popular destinations for trafficked women are countries
where prostitution is legal such as the Netherlands (Hughes 2001). According to
Shelley et al. (1999) most of these women are in conditions of slavery. Girls of Dutch
nationality who were forced to work in prostitution in Amsterdam typically fell prey
to a loverboy. The loverboy is a relatively new phenomenon (Bovenkerk et al. 2004)
in the Netherlands. A loverboy is a man, mostly with Moroccan, Antillean or Turkish
roots who makes a girl fall in love with him and then uses her emotional dependency
to force her to work as a prostitute.
In this paper we report on our Formal Concept Analysis (FCA)-based (Ganter et al.
1999) efforts for identifying unknown suspects and victims of human trafficking and
forced prostitution in the police region Amsterdam-Amstelland in the Netherlands.
Since the introduction of Intelligence Led Policing (Collier 2006, Viaene et al. 2009)
in 2005, a management paradigm for police organizations which aims at gathering
and using information to allow for pro-active identification of suspects, police officers
are required to write down everything suspicious they noticed during motor vehicle
inspections, police patrols, etc. These observational reports, 34,817 in 2005, 40,703 in
2006, 53,583 in 2007, 69,470 in 2008 and 67,584 in 2009, may contain indications
that can help reveal individuals who are involved in human trafficking, forced
prostitution, terrorist activities, etc. However, till date almost no analyses were
performed on these documents.
We first used concept lattices to visualize the observational reports and distill
interesting indicators and concepts that can be used for tracking down suspects. For
each person mentioned in these reports, a document vector was constructed
containing all relevant attributes or indicators that were found in the data. This
concept lattice in which all available information for each person was gathered,
revealed some cases where there were sufficient indications for starting an in-depth
investigation. We applied FCA and its temporal variant to zoom in on some real life
cases and suspects, resulting in actual arrestments being made and/or illegal
prostitution locations closed down.
In section 2 we give background information on human trafficking, forced
prostitution and the guidelines that were developed by the Attorney Generals of the
Netherlands to help detect trafficking and loverboy suspects. In section 3 we describe
the dataset. In section 4 we describe our analysis method to detect and profile
potential suspects. In section 5 we describe some real life cases where the suspects
were found with FCA. Finally, section 6 concludes the paper.
2. Human trafficking and forced prostitution
Victims of human trafficking rarely make an official statement to the police. The
human trafficking team of the Amsterdam-Amstelland police is installed to
proactively search police databases for any signals of human trafficking.
Unfortunately, this turns out to be a laborious task. The investigators have to
manually read and analyze the police reports, one by one, because only an estimated
15% of the information containing human trafficking indications has been labeled as
such by police officers. As soon as the investigators find sufficient indications against
a person, a document based on section 273f of the code of criminal law is composed
for the person under scrutiny. Based on this report, a request is sent to the Public
Prosecutor to start an in-depth investigation against the potential suspects. After
permission is received from the Public Prosecutor, the use of special investigation
techniques such as phone taps and observation teams is allowed.
The following list contains the types of indications mentioned in the guidelines
developed by the Attorney Generals of the Netherlands based on which police forces
can gather evidence of human trafficking and forced prostitution against potential
suspects. These guidelines define in which cases pro-active intervention by police
may be necessary. This information had not yet been used to actively search police
databases for suspicious activity reports containing human trafficking indicators.
1. Dependency on exploiter: Typically in human trafficking the housing, clothing and
transportation of the woman are arranged through the exploiter, the woman will
often have debts towards the exploiter and will be forced to earn the money back.
2. Deprivation of liberty: Often the victim is not allowed to have contact with the
outside world. She typically does not have her passport with her which is carried
by the pimps.
3. Being forced to work under bad circumstances: The victim has to work for many
hours, cannot freely dispose of the money she earns, etc.
4. Violation of bodily integrity of the victim: The victim is forced to work as a
prostitute through physical violence, threatening, etc.
5. Non-incidental pattern of abuse by suspect(s) can be observed.
3. Dataset
Our dataset consists of 266,157 suspicious activity police reports, 34,817 in 2005,
40,703 in 2006, 53,583 in 2007, 69,470 in 2008 and 67,584 in 2009. These police
reports are stored in the police databases as unstructured text documents and have the
following associated structured data fields: title of the incident, project code assigned
by the responsible officer, location of the incident and optionally a formally labeled
suspect, victim and/or other involved persons. The unstructured part of these
suspicious activity reports describes observations made by police officers during
motor vehicle inspections, during a police patrol, when a known person was seen at a
certain place, etc. These reports were extracted from the database and turned into html
documents that were indexed using the open source engine Lucene.
The thesaurus constructed for this research contains the terms and phrases used to
detect the presence or absence of indicators in these police reports. This thesaurus
consists of two levels: the individual search terms and the term cluster level which
was used to create the lattices in this paper. We used a semi-automated approach as
described in (Poelmans et al. 2010a). Search terms and term clusters were defined in
collaboration with experts of the anti-human trafficking team and gradually improved
by validating their effectiveness on subsets of the available police reports. Each of
these search terms were thoroughly analyzed for being sufficiently specific. The
quality of the term clusters was determined based on their completeness. The
validation of the quality of the thesaurus and the improvements were done by us and
in conjunction with members of the anti-human trafficking team. Concept structures
were created on multiple randomly selected subsets of the data. It was manually
verified if all relevant indicators were found in these reports and no indicators were
falsely attributed to these reports. For example, the term cluster “prostitute” in the end
contained more than 20 different terms such as “prostituee”, “dames van lichte
zeden”, “prosti”, “geisha”, etc. used by officers to describe a prostitute in their textual
reports. To create the formal contexts in this paper, the term clusters in the thesaurus
were used as attributes and the police reports as objects. A prototype of the FCA-
based toolset CORDIET (which is currently being developed under a collaboration
between KULeuven and Moscow Higher School of Economics) was used during the
analysis process (Poelmans et al. 2010d).
4. Method
Our investigation procedure consists of multiple iterations through the square of Fig.
1. For background information on FCA and its applications in KDD we refer the
reader to Poelmans et al. (2010c). The guidelines of section 2 contain a non-
limitative list of indications and the indications can be subdivided into 5 main
categories. If at least one of the thesaurus elements corresponding to these indications
is present for a person or a group of persons, we might be dealing with a case of
human trafficking or forced prostitution. From the 266,157 reports in our dataset, the
relevant reports which contain at least one indicator are selected. Then, the persons
mentioned in these reports are extracted and FCA lattices are created, showing all the
indications observed for each person. From these lattices containing persons, potential
suspects or victims can be distilled and they can be further analyzed in detail with
FCA and temporal concept lattices. If sufficient indications are available, a document
based on article 273f of the code of criminal law can be created and sent to the Public
Prosecutor with the request for using advanced intelligence gathering instruments
such as observation teams, phone taps, etc. If the suspects are indeed involved in
human trafficking and forced prostitution they can be taken into custody.
Fig. 1. Criminal intelligence process
Our method based on FCA consists of 4 main types of analysis that are performed:
• Concept exploration of the forced prostitution problem of Amsterdam: In
(Poelmans et al. 2010a, Poelmans et al. 2010b) our FCA-based approach for
automatically detecting domestic violence in unstructured text police reports
is described in detail. We not only improved the domestic violence definition
but also found multiple niche cases, confusing situations, faulty case
labelings, etc. that were used to amongst others improve police training. Part
of the research reported on in this paper such as the construction of the
thesaurus, consisted of repeating the procedures described in our domestic
violence case study papers.
• Identifying potential suspects: Concept lattices allow for the detection of
potentially interesting links between independent observations made by
different police officers. When grouping suspicious activity reports on a per
person basis, the available information about the individuals is displayed in
one intuitive and understandable picture that facilitates efficient decision
making on where to look. In particular persons lower in the lattice can be of
interest since they combine multiple early warning indicators.
• Visual suspect profiling: Some FCA-based methods such as Temporal Concept
Analysis (Wolff 2005) were developed to visually represent and analyze data
with a temporal dimension. Temporal Concept lattices were used in (Elzinga
et al. 2010) to create visual profiles of potentially interesting terrorism
subjects. Scharfe et al. (2009) used a model of branching time in which there
are alternative plans for the future corresponding to any possible choice of a
person and used it as the basis of an ICT toolset for supporting autism
diagnosed teenagers. For creating the temporal profile of individual suspects,
we use traditional FCA lattices and the timestamps of the police reports on
which these lattices are based are used as object names. The nodes of the
concept lattice can then be ordered chronologically.
• Social structure exploration: Concept lattices may help expose interesting
persons related to each other, criminal networks, the role of certain suspects
in these networks, etc. With police officers we discussed and compared
various FCA-based visualization methods of criminal networks. Individual
police reports mentioning network activity were used by us as objects and
the timestamps of these police reports together with each suspect name
mentioned in these reports as object names.
5. Analysis and results
Traditional data mining techniques often focus on automating the knowledge
discovery process as much as possible. Since the detection of actual suspects in large
amounts of unstructured text police reports is still a process in which the human
expert should play a central role, we did not want to replace him, but rather empower
him in his knowledge discovery task. We were looking for a semi-automated
approach and in this section we try to illustrate the main reasons why FCA was ideal
for this type of police work. With FCA at the core, we were able to offer police
officers an approach which they could use to interactively explore and gain insight
into the data to find cases of interest to them on which they could zoom in or out.
Section 5.1 shows a lattice diagram which was of significant interest to investigators
of the anti-human trafficking team. For the first time, the overload of observational
reports was transformed into a visual artifact that showed them a set of 1255 persons
potentially of interest to the police and the indicators observed for each of them. The
lattice diagram visually summarizes the data and makes it more easily accessible for
officers who want to efficiently explore it and extract unknown suspects. We chose to
first highlight the case of the Turkish human trafficking network in section 5.2. From
the lattice diagram in section 5.1, two potential suspects were distilled since they were
regularly spotted performing illegal activities. We found the name of a bar was
mentioned a couple of times and used this information to build the concept lattice of
section 5.2. This lattice diagram was of particular interest to police officers since FCA
quickly gave them a concise overview of the persons that were observed to be
involved around a suspicious location and the lattice structure helped them to identify
the most important suspects in this network. In particular the visualization of persons
in a lattice was helpful during their exploration. FCA's partial ordering gave them
clues on where to look first. The lower a person appears in the lattice, the more
indicators he has. Section 5.3 showcases how the FCA visualization was used to
combine temporal and social structure information in one easy to interpret picture.
Such profile lattices were of significant interest to police officers since they allow for
quick decision making on whether or not a person might be involved in illegal
activities. Moreover, the lattices may help infer the roles of the persons mentioned in
the network. Finally section 5.4 shows how an FCA lattice can give insight into the
evolution of a person over time, in this case of a loverboy. The remaining part of this
section describes cases of human trafficking and forced prostitution and two of them
were identified in the lattice in Fig. 2 and further investigated with FCA. Note that
real names were replaced by false names because of privacy reasons.
5.1 Detection of suspects of human trafficking and forced prostitution
Fig. 2. Human trafficking suspect detection lattice diagram
Multiple concept lattices were created for detecting human trafficking suspects in the
set of persons. Each of these concept lattices contained over 200 concepts and were
based on different combinations of attributes. Since the format of this paper does not
allow to visualize the entire lattices in a readable way, we chose to simplify one of
these lattices and zoomed in on its most important aspects. Fig.2. contains the lattice
diagram with 1255 Bulgarian, Hungarian and Romanian persons. The concept
containing some of the suspects of section 5.2 was found on the right and bottom part
of the lattice and has 10 persons in its extent. The concept containing the main suspect
of section 5.3 was found on the left and bottom part of the lattice and has 1 object in
its extent. The following 2 sections will be used to describe and profile each of these
suspects in detail.
5.2 Case 1: Turkish human trafficking network
By analyzing the concept lattice based on observational reports, we were able to
expose a criminal network operating in Amsterdam, involved in illegal and forced
prostitution. The concept lattice diagram in fig. 3 contains the 61 persons and
indicators found in the police reports mentioning activity around a bar in Amsterdam
that played a central role in the network's activities and was closed down in 2009.
Multiple suspects operating in this network were found and some of the observations
will be described in this section. The most important suspects are the persons with
indication legitimation problems, since they were carrying the id papers of the girls.
The police reports contained many indications of illegal and forced prostitution taking
place, activities that were run by the owners or acquaintances of the owners of the bar.
We found out the bar was used as a central hub, where mostly Turkish men met up
with Bulgarian girls who had been forced into prostitution and took them to another
location. We found at least two pimps who have multiple girls working for them.
Fig. 3. Concept lattice diagram of human trafficking network
Starting in 2007, the first observations were made that hinted at illegal and forced
prostitution being organized from within this bar. On 2 June 2008, victim H declared
to the police that she was forced to work as a prostitute in the bar and did not get any
money for that. She was never allowed to leave the house alone and the door of her
apartment was locked from the outside such that she couldn't leave. On 12 December
2008, suspect A came out of the bar with a girl, their statements to the police did not
match and moreover the girl was dressed in sexy clothing. Most likely the girl works
as a prostitute and the driver is her pimp. On 25 January 2009, police officers stopped
a car and behind the wheel was suspect B and next to him the victim E. We found
woman E is often sitting at the bar and also the car is regularly parked in front of the
bar. Suspect B gave the passport of victim E to the police and afterwards he placed it
back in his pocket. Moreover, suspect B was carrying a large amount of cash money,
1000 euros in his pocket. On 26 January 2009, police did a check-up on the guests in
the bar. One girl was new and told she only just arrived by train, she had no train
tickets with her and she did not know her living address. Suspect B was also there and
told the police he is a car trader so he travels a lot between Bulgaria and Netherlands.
An excuse typically used by criminals responsible for the logistics of a trafficking
network. Also victim E and two other girls, victims F and G were there. On 20
February 2009, police officers saw suspect A talking to the driver of a car with
Bulgarian license plate. Afterwards he forced a girl to follow him and when the police
asked about their relationship they told they had been friends for 3 months. The girl
did not have her id-papers with her and the police went to her living address. In the
house there were many mattresses and another girl. Both of them told they have no
job. Most likely the house serves as an illegal prostitution location for the criminal
gang.
Sufficient indications were found and on 17 June 2009, an observation team
observed the bar during the evening. Eastern European women were sitting at the bar
and mostly Turkish, Moroccan and Eastern European men at the tables. During the
evening, the team saw multiple girls that were taken out of the bar by a customer to a
hotel, house, etc. and brought back to the bar afterwards. On 15 July 2009 sufficient
evidence was gathered that illegal prostitution was organized from within this bar and
authorities closed down the bar.
5.3 Case 2: Bulgarian male suspect
In this section we describe a profile of a Bulgarian suspect who was also operating in
Amsterdam. The lattice diagram in Fig. 4 shows that on 3 October 2007, suspect A
was observed for the first time during a police patrol. An officer told the driver of a
BMW car with Bulgarian license plate to turn right instead of left, the driver however
ignored the instructions he received and quickly drove to the left with squeaking tires.
The officer went after and in the end stopped the car. There were 3 men and one
woman in the car. Suspect B was the driver and suspect A was sitting next to him. On
the backseat of the car were woman F and man K. They told the officer they only
arrived 3 days ago in the Netherlands and are a couple. Suspect A and suspect B were
taken to the police office; man K and woman F walked away and were followed by a
second officer. He saw that K was strongly holding the hand of F and forced her into
a home at the corner of a street in central Amsterdam. In the police office, suspect B
was not able to tell the address of the apartment he was going to rent. Suspect A was
carrying a large amount of cash money in his pocket.
Fig. 4. Profile lattice diagram of individual suspect and his network
On 30 June 2009, woman J went to the police to ask if they could supervise the
undersigning of a tenancy agreement of an apartment by man M who promised her
accommodation. She told suspect A was intimidating and trying to scare away man M
because suspect A wanted to rent the apartment for prostitution purposes. She was
very afraid of suspect A and the officer noted that she might have been forced in
prostitution by him. On 30 October 2007, the police did a routine inspection of 2
individuals who were waiting with two motorcycles in a street that had been plagued
by street robberies. This was the second observation of suspect A by the police and
his motorcycle was registered by the name of woman C who had been involved in
human trafficking activities as a victim. On 6 March 2009 the police received a tip
that a fugitive Colombian criminal might be living at a certain address owned by
professional criminal H. When they entered the apartment they found 2 men and 2
women of Bulgarian nationality. Man X and woman C declared to be on holiday and
would go back to Bulgaria although we found suspect A was driving around with a
scooter registered at C's name in 2007. Man Y declared he exports expensive cars to
Bulgaria and regularly drives back and forth between Netherlands, an excuse typically
used by suspects taking care of logistics of a human trafficking gang. Woman Z
declared to work in prostitution in Groningen. When the officers left the apartment
they found a motorcycle registered on the name of suspect A. The last observation
dates back to 17 April 2009 when the police saw suspect A call somebody while
standing in the entrance hall of prostitute R. He tells the police he has nothing to do
with prostitution and owns a restaurant in Bulgaria. After his phone call he gives the
cell phone to the prostitute.
To conclude, suspect A and B are most likely involved in human trafficking and
there were sufficient signals found to request the use of special investigation
techniques. Permission was granted, our suspicions were confirmed and both A and B
were arrested by the police in 2010. Moreover these lattices showed some other
people who are involved in the same gang and could be monitored.
5.4 Case 3: Loverboy suspect
In this section we describe a loverboy case which we exposed by gathering evidence
from multiple observational reports. This person was not found by analyzing the
lattice diagram in Fig. 2 but by investigating a lattice based on Antillean, Moroccan
and Turkish persons. Victim V is a girl of Dutch nationality who officially lived in the
Netherlands but fell prey to a loverboy of originally Antillean nationality. We found
multiple indications in filed suspicious activity reports that referred to elements of the
model in section 2. The lattice diagram of suspect A and victim V is displayed in Fig.
5.
On 27-04-2006, Suspect A and victim V were noticed for the first time on the
streets during a police patrol. They had a serious argument with each other and
suspect A took the cell phone with force out of V's hand. When the police intervened
they claimed nothing happened. In the police station she declared that she works
voluntarily in prostitution although her words were not convincing to the officer. On
15-08-2006 an Amsterdam citizen sent an email to the police about young Antillean
men who constantly surveillance some women in the red light district. Amongst other
suspect A brings food and drinks to the women who are not allowed to leave their
rooms. On 31-10-2006 during a police patrol, victim V was noticed while she got out
of a car and quickly ran inside. The driver of the car was suspect A. She told the
police later on that she was brought to and picked up every day at this apartment by
her boyfriend suspect A. The police noticed her dismayed and timid attitude and
asked again if she was forced to work in prostitution. In a non-convincing way she
responded that she did her job voluntarily. On 15-09-2006, suspect A had to stay in
jail for 6 hours because of illegal weapon possession. When the police asked about his
income he told he earned good money thanks to his girlfriend who works in
prostitution. On 2-11-2006, officers noticed the car of victim V was parked on the
road and two Negroid men were inside. The driver, suspect A got out of the car and
yelled to the girl he was picking up at her apartment, that she had to hurry up. The
whole scene looked very intimidating to the police and it turned out the girl was
victim V. Suspicious was that the car was registered on the name of V while V had no
driver license. On 28-03-2007, victim B came to the police office to ask if she was
allowed to work with a badly damaged id-document or if she had to wait for a new
one. She mentioned that suspect A was her ex-boyfriend and that she and victim V
were the victim of extortion but she did not dare to make an official statement to the
police. Afterwards, the police checked a home where they found 2 women: victim V
and B. Victim V had a big tattoo on her right shoulder and a smaller tattoo on her
upper arm. On 19-08-2007, suspect A was involved in a knifing incident in the red
light district between 3 men and one of these men got seriously injured. This man
wanted sex with victim V but suspect A did not allow this because of the man's
ethnicity, which caused the fight. On the camera surveillance videos, victim V was
observed to accompany suspect A all the time. On 16-10-2007, officers observed that
suspect A who walked over the streets said hi to all women who passed by.
Fig. 5. Profile diagram of loverboy suspect
6. Conclusions
Textual documents contain a lot of useful information that is rarely turned into
actionable knowledge by the organizations that own these data repositories. The
police of Amsterdam-Amstelland disposes of a large amount of such textual reports
that may contain early warning indicators that can help to proactively identify persons
involved in illegal activities. Since the observations of one suspect are typically made
by different officers who are not aware of each others work, spread over multiple
databases, etc. automated analysis techniques such as FCA can be of significant
importance for police forces who are interested in the proactive identification of
perpetrators. FCA is one of the few techniques that can be used to interactively
expose, investigate and refine the underlying concepts and relationships between them
in a large amount of data. In this paper we described our successful application of
FCA to find suspects of human trafficking and forced prostitution in the Amsterdam-
Amstelland police district. From 266,157 observational reports we distilled multiple
suspicious cases of which 3 have been described in this paper. For each of these
persons and networks we composed a document containing all the indicators and
evidence available and sent this to the Public Prosecutor. Permission to use special
investigation techniques was obtained by the anti-human trafficking team based on
these documents. For each case we exposed, phone-taps, observation teams, etc.
indeed confirmed the suspect’s involvement in human trafficking and forced
prostitution. We believe that in making the shift from reactive police work, where
action is only undertaken when a victim comes to talk directly to the police, to the
pro-active identification of suspect’s, FCA can play an important role.
Acknowledgements
The authors would like to thank the police of Amsterdam-Amstelland for granting
them the liberty to conduct and publish this research. In particular, we are most
grateful to Deputy Police Chief Reinder Doeleman and Police Chief Hans Schönfeld
for their continued support. Jonas Poelmans is aspirant of the Fonds Voor
Wetenschappelijk Onderzoek – Vlaanderen or Research Foundation – Flanders.
References
1. Bovenkerk, F., Van San, M., Boone, M., Van Solinge, T.B., Korf, D.J. (2004)
“Loverboys” of modern pooierschap in Amsterdam. Willem Pompe Instituut voor
Strafwetenschappen, Utrecht, December.
2. Collier, P.M. (2006) Policing and the intelligent application of knowledge. Public
money & management. Vol. 26, No. 2, pp. 109-116.
3. Elzinga, P., Poelmans, J., Viaene, S., Dedene, G., Morsing, S. (2010) Terrorist
threat assessment with Formal Concept Analysis. Proc. IEEE International Conf.
on Intelligence and Security Informatics. May 23-26, Vancouver, Canada. 77-82.
4. Equality Division, Directorate General of Human Rights of the Council of Europe
(2006) Action against trafficking in human beings: prevention, protection and
prosecution. Proceedings of the regional seminar, Bucharest, Romania, 4-5 April.
5. Ganter, B., Wille, R., Formal Concept Analysis: Mathematical Foundations.
Springer, Heidelberg, 1999.
6. Hughes, D.M. (2000) The “Natasha” Trade: The transnational shadow market of
trafficking in women. Journal of international affairs, Spring, 53, no. 2. The
trustees of Colombia University in the City of new York.
7. Hughes, D.M., Denisova, T. (2003) Trafficking in women from Ukraine U.S.
Department of Justice research report.
8. O’Neill, RA. (1999) International trafficking to the United States: a contemporary
manifestation of slavery and organized crime- and intelligence monograph.
Washington DC; Exceptional Intelligence Analyst Program.
9. Poelmans, J., Elzinga, P., Viaene, S., Dedene, G. (2010) Curbing domestic
violence: Instantiating C-K theory with Formal Concept Analysis and Emergent
Self Organizing Maps. Intelligent Systems in Accounting, Finance and
Management 17, 167-191. Wiley and Sons, Ltd. Doi 10.1002/isaf.319.
10.Poelmans, J., Elzinga, P., Viaene, S., Dedene, G. (2010) Formally Analyzing the
Concepts of Domestic Violence, Expert Systems with Applications 38, 3116-3130.
Elsevier Ltd. doi 10.1016/j.eswa. 2010.08.103 .
11.Poelmans, J., Elzinga, P., Viaene, S., Dedene, G. (2010), Formal Concept Analysis
in Knowledge Discovery: a Survey. LNCS, 6208, 139-153, 18th int. conf. on
conceptual structures (ICCS). 26 - 30 July, Kuching, Sarawak, Malaysia. Springer.
12.Poelmans, J., Elzinga, P., Viaene, S., Dedene, G. (2010). Concept Discovery
Innovations in Law Enforcement: a Perspective, IEEE Computational Intelligence
in Networks and Systems Workshop (INCos 2010), Thesalloniki, Greece.
13.Shelley, L. (1999) Human trafficking: defining the problem . Organized crime
watch-Russia, Vol. 1, No. 2, February.
14.Scharfe, H., Oehrstrom, P., Gyori, M. (2009): A Conceptual Analysis of Difficult
Situations – developing systems for teenagers with ASD. Suppl. Proc. Of the 17th
Int. Conf. On Conceptual Structures (ICCS), Moscow, Russia.
15.U.S. Department of State (2008) Trafficking in persons report.
http://www.state.gov/g/tip/rls/tiprpt/2008, retrieved on 26-12-2010.
16.United Nations, Economic and social council (2004) Economic causes of
trafficking in women in the Unece region. Regional Preparatory Meeting, 10-year
review of implementation of the Beijing Platform for Action, 14-15 December.
17.Viaene S., De Hertogh S., Lutin L., Maandag A., den Hengst S., Doeleman R.
(2009). Intelligence-led policing at the Amsterdam-Amstelland police department:
operationalized business intelligence with an enterprise ambition. Intelligent
systems in accounting, finance and management. 16 (4) : 279 -292.
18.Wolff, K.E., (2005) States, transitions and life tracks in Temporal Concept
Analysis. In: B. Ganter et al. (Eds.): Formal Concept Analysis, LNAI 3626,
Springer, Heidelberg, 127-148.
