Download Border Security up Gradation using Data Mining

International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-4, Issue-ICCIN-2K14, March 2014
ICCIN-2K14 | January 03-04, 2014
Bhagwan Parshuram Institute of Technology, New Delhi, India
Border Security up Gradation Using Data Mining
S.S. Prasad, Sonali Mathur, Sonal Singh
Secure and efficient border security is best achieved
through an integrated and intelligence-led approach with
effective interventions pre-arrival, at the border and incountry. The best border security is delivered through
integrated technology enabling effective sharing of data,
assured identity management and strong inter-agency cooperation and related processes [3].
While monitoring millions of border crossings each year,
Border Security personnel are required to balance
operational efficiency and security concerns. As vehicles or
individuals enter the country, it is necessary to record each
license plate and each passport number with a crossing date
and time. It is also necessary to search vehicles and
individuals for drugs and other contraband. The value of the
collected data could be enhanced by combining it with the
millions of relationships recorded between people, places,
vehicles, and incidents in local law enforcement records
management systems, but usefully integrating the data is a
difficult task. In spite of an obvious connection between
criminal and border crossing activities, such data is only
combined in special cases when investigators call each
other and ask for help. This kind of sharing is timeconsuming and therefore not very frequent [8].
Data sharing has important implications both for homeland
security and local law enforcement. Suspicious activity and
other reports from locations near critical infrastructure sites
can have national security implications. Local law
enforcement officials may have data related to terrorists
without knowing that which individuals are terrorists.
Border agencies are interested in certain individuals but
have no efficient way to check with local authorities.
In this research work a software tool has been developed
that can be delivered at the border check post. The main
objective of this research work is to design a system that
can fill the void between local law enforcement departments
and border agencies by data sharing, using data mining
techniques for collecting information of crimes and
criminals from various departments, integrating that
information and make it available for all the departments
and border check posts in order to check it against every
passenger and vehicles crossing the border. This research
work develops a methodology that helps in identifying
important investigative leads with the help of Criminal
Activity Network so as to uncover important and unnoticed
patterns and links between people, vehicles, criminal
incidences and border crossing activities. This system also
helps to outline a graph of criminal activity rate so as to
draw more attention at borders in the month having highest
percentage of criminal activities
Abstract— Homeland security concerns have identified border
and transportation security as critical areas. The strategy for
homeland security calls for enhancement of the security checks
at borders without inordinate delay. This system is designed for
creating a Criminal Activity Network (CAN)s, which is a
framework of effectively integrated information collected from
local, state and central sources, and for outlining a crime rate
graph month wise, all using Data Mining as a useful tool for
pattern analyzing, tracking, detecting and preventing terrorism.
This work develops a methodology for identifying important
investigative leads by analyzing relationships between people,
vehicles, criminal incidents and border crossing activities and
thus sharing as well as updating the information among all
departments. The experimental results showed our system
performs sufficiently well to be used in real world settings,
including as an aid for counter terrorism, which is our overall
aim.
Keywords— Fuzzy C- Mean algorithm, Fuzzy Clustering,
Clustering, Data mining, Pattern analysis.
I.
INTRODUCTION
Data mining has become one of the key features of many
homeland security initiatives. Often used as a means for
detecting fraud, assessing risk, and product retailing, data
mining involves the use of data analysis tools to discover
previously unknown, valid patterns and relationships in
large data sets. In the context of homeland security, data
mining can be a potential means to identify terrorist
activities, such as money transfers and communications,
and to identify and track individual terrorists themselves,
such as through travel and immigration records.
Data mining is defined as the nontrivial process of
identifying valid, novel, potentially useful, and ultimately
understandable patterns in data. Once these patterns are
identified they can be used to enhance decision making in a
number of areas.
“Data mining” is one technique that has significant
potential for use in countering terrorism. Data-mining and
automated data-analysis techniques are not new; they are
already being used effectively for different applications.
Recently there has been much interest on exploring the use
of data mining for counter terrorism applications. For
example, data mining can be used to detect unusual
patterns, terrorist activities and fraudulent behavior. Data
mining is increasingly becoming a useful tool for tracking,
detecting and preventing terrorism. Protecting our borders
from the illegal movement of weapons, drugs, contraband,
and people, while promoting lawful entry and exit, is
essential to homeland security, economic prosperity, and
national sovereignty.
II. LITERATURE REVIEW
Manuscript received March 2014
S.S. Prasad, Department of MCA, Department of Computer Science,
Department Of Computer Science, J.S.S Academy of Technical Education,
Noida, Mahamaya Technical University C-22, Sector62,Noida , Uttar
Pradesh, India.
Sonali Mathur, Department of MCA, Department of Computer
Science, Department Of Computer Science, J.S.S Academy of Technical
Education, Noida, Mahamaya Technical University C-22, Sector62,Noida , Uttar Pradesh, India.
Sonal Singh, Department of MCA, Department of Computer Science,
Department Of Computer Science, J.S.S Academy of Technical Education,
Noida, Mahamaya Technical University C-22, Sector62,Noida , Uttar
Pradesh, India.
A. Data Collection, Integration and Sharing
For strengthening border security the most important things
are data collection, data integration and data sharing. To
identify whether a person or vehicle is involved in any
illegal activities and whether to allow them to cross the
border or not, it’s very important to utilize the information
from multiple sources.
Firstly it is hard to collect this data from several
departments since all the data of criminals and vehicles
involved in criminal activities, is highly
55
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.
Border Security Up Gradation Using Data Mining
sensitive and confidential. Secondly combining that
collected data from independently- developed sources is a
challenging task [10]. Commonly acknowledged problems
in integrating data collected from several departments
include [10][8]: (1) Name Differences: same name,
different entity, (2) Missing Data: incomplete sources or
different data available from different sources, and (3)
Object Identification: no global ID values and no interdatabase ID tables. After successfully collecting and
integrating data of criminals and vehicles, most important
task is to make a system which can make integrated data
available for all the departments i.e. making a common
management system which can be accessed by all
departments, problems acknowledged here is that such data
is very vast and thus making a system which can efficiently
work on large data and keep all data safe is a robust task.
By utilizing the information available in the system, it is
possible to identify, whether individual or vehicle crossing
the border is having any criminal record and thus whether
to allow them to cross the border or not.
jurisdictions with multiple object and relation types, (2)
high volume but relatively simple supplementary data to
enhance CAN information content, and (3) case specific or
ad-hoc query-specific data expressing important
relationships or features. Given these classes of data,
integration should proceed in three steps: schema-level
transformation of base data, entity-matching to align objects
across data sets, and normalization and matching of
supplementary data.
Base data should be semantically aligned and mapped to
support CAN generation. When there is a lot of overlap
between datasets, there is a lot of value to be gained. This is
a classic data integration task requiring reconciliation of
legacy data into a common schema and instance-level entity
matching. Police records are the prime example of this kind
of data because multiple jurisdictions keep similar types of
data about an overlapping set of objects. Standardized data
dictionaries may eventually encourage development of
interoperable systems, but for now data sharing initiatives
generally begin by mapping to a global schema and then
move on to entity matching. Base data integration should be
a repeatable transformation process so that the combined
datasets can be refreshed frequently.
Entity matching in this domain will tend to rely on
heuristics. Primary objects will include people, location and
vehicles. Input from domain experts suggests an initial
match for people using Passport number. Other alternatives
may be useful but are not consistently available and
vehicles can be matched by license plate and/or vehicle
identification number (VIN).
License plate data has some interesting and useful
characteristics. Plate numbers can be recorded in an
unobtrusive fashion and, while criminals frequently avoid
identification by lying about their names in routine
interactions with law enforcement officials, license plate
numbers are directly observed. In addition, vehicles used by
criminals are often registered in someone else’s name. Even
if a criminal uses an alias in incidents involving a particular
vehicle, the resulting person-vehicle data implicitly links
the incidents. License plate numbers also are occasionally
transferred to different cars: illegally when a car or plate is
stolen or legally when it is sold. For many applications
these characteristics make plate numbers more useful than
vehicle identification numbers.
In addition to the base data, investigators use many
additional supplementary or query-specific information
resources to identify criminals’ activities and associations.
This additional data may not be readily available for a
variety of reasons.
• Specialization: Frequently, useful data is not directly
accounted for in the global schema. For example,
police systems do not usually store border-crossing
events.
• Availability: Frequently, information like jail visitation
histories and motor vehicle registration records are
important and could be, but haven’t been, included in
an agency’s data system.
• Sensitivity: Investigators do not want many bits of
information included in widely used sources. In some
cases it is feared that information would be leaked to
the criminals involved. In some cases data has been
demanded legally and can be used only in a single
investigation.
• Contextual usefulness: Background information and
rumors identify some relationships between individual
criminals, for example, “Ashok and Karan are
brothers” or “Ram and Raghav were friends in high
school”. This kind of information is not collected in
large quantities, applies only to specific cases, and
should not be included in police records because of
privacy and security concerns.
B. Fuzzy C- Mean Clustering
Cluster analysis or clustering is the task of grouping a set of
objects in such a way that objects in the same group (called
a cluster) are more similar in some sense to each other than
to those in other groups (clusters) [8]. It is a main task of
exploratory data mining, and a common technique
for statistical data analysis, used in many fields,
including machine learning, pattern recognition, image
analysis, information retrieval, and bioinformatics.
Clustering
can
be
considered
as
the
most
important unsupervised learning problem; so, as every other
problem of this kind, it deals with finding a structure in a
collection of unlabeled data. It can be achieved by various
algorithms that differ significantly in their notion of what
constitutes a cluster and how to efficiently find them.
Popular notions of clusters include groups with
small distances among the cluster members, dense areas of
the data space, intervals or particular statistical
distributions. Clustering can therefore be formulated as
a multi-objective optimization problem. The appropriate
clustering algorithm and parameter settings (including
values such as the distance function to use, a density
threshold or the number of expected clusters) depend on the
individual data set and intended use of the results. Out of
many algorithms of clustering we choose Fuzzy C-Mean
(an overlapping clustering algorithm) algorithm for this
research work. Since it gives best result for overlapped
dataset and comparatively better than K-means algorithm.
Unlike K-means where data point must exclusively belong
to one cluster centre, here data point is assigned
membership to each cluster centre as a result of which data
point may belong to more than one cluster centre.
Fuzzy C-Means (FCM) is a method of clustering which
allows one piece of data to belong to two or more clusters.
This method (developed by Dunn in 1973 and improved
by Bezdek in 1981) is frequently used in pattern
recognition. In fuzzy clustering (also referred to as soft
clustering), data elements can belong to more than one
cluster, and associated with each element is a set of
membership levels. These indicate the strength of the
association between that data element and a particular
cluster [9][10]. Fuzzy clustering is a process of assigning
these membership levels, and then using them to assign data
elements to one or more clusters. Using Fuzzy C-Mean
algorithm system created Criminal Activity Network
(CAN).
C. Cross-Jurisdictional Integration Framework
The key to the framework is identification of three classes
of data: (1) base data with overlapping data from multiple
56
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.
International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-4, Issue-ICCIN-2K14, March 2014
ICCIN-2K14 | January 03-04, 2014
Bhagwan Parshuram Institute of Technology, New Delhi, India
the authentic person at the border enters the ‘passport
number’ for person and ‘vehicle number’ for vehicle in the
software and then software verifies it with the available
database.
If a match is not found in the database, software will
display “happy journey” message and new record gets
registered in the border security database.
If a match is found in the database, software will display
“record found” message with providers information i.e. the
department and place from where the record was found e.g.
“Delhi criminal found call Delhi police” or “Delhi vehicle
found call Delhi police”
Once the criminal record is found, the border security’s
authentic user retrieves criminal’s information including
names, age, sex, address, driving license number, passport
number, criminal activity they were involved in and their
current status or vehicle’s information including vehicle
number, chassis number, vehicle type, color, criminal
activities they were involved in and their current status with
the help of “manage” option provided in the software.
After receiving all information regarding criminal or
vehicle involved in criminal activities, authentic user
updates the particular department from where the record
was found using “send update” option which includes
“caught at, passport number or vehicle number”.
This update sent by Border Security agency will be notified
to the Investigative Agency and Police department
whenever they will access their respective login ids’.
Depending upon all the data collected from both the
departments (Police, Investigative Agency) and its own
records and findings, this software will help authentic
person of border security agencies to draw a Criminal
Activity Network(CAN) using Fuzy C-Mean algorithm of
Clustering with a “CAN” option provided in the software.
CAN will help border security agencies in identifying
important investigative leads by analyzing known
relationships between people, vehicles, criminal incidents
and border crossing activities.
This software, with the help of CAN also facilitates
authentic person of border security agencies to outline a
graph representing percentage of criminal activities in each
month using “Bar Chart” option provided in the software.
III. RESEARCH DATASET
A. Dataset
For strengthening border security the most important tasks
are data collection, data integration and data sharing. To
identify whether a person or vehicle is involved in any
illegal activities and whether to allow them to cross the
border or not, it’s very important to utilize the information
from multiple sources.
In this research work information from many data sources
has been utilized. These data sources are:
Police record of criminals which includes names, age, sex,
address, driving license number, passport number, criminal
activity they were involved in and their current status.
Record of vehicles involved in criminal activities which
include vehicle number, chassis number, vehicle type,
color, criminal activities they were involved in and their
current status.
Border crossing data records which includes information of
all passengers and vehicles crossing the border.
Since all this data which was required in this research work
was highly sensitive and such data is confidential, so to
carry out this research work we have created dummy
databases. All the research work is based on dummy data
base.
B. Research Design
Our framework allows for the inclusion of this kind of data
by treating it as supplementary data or as query-specific
data. A data source is appropriate for supplementary
integration when (1) it is available in quantity and can be
appropriately organized, (2) its sensitivity level allows for it
to be shared across multiple investigations, and (3) it is
contextually appropriate outside of a single investigation.
Data can be used as supplementary data if it can be reduced
to one or more lists of features or events directly associated
with identifiable objects in the base data set. For example,
border crossing records, or jail visitations can all be
recorded associated with particular individuals already
contained in a base data set of criminal incidents. Queryspecific data can be used to guide CAN building. For
example if phone records indicate a suspect called 19
different people, a CAN network could query for
relationships involving any of the 20 people to arrive at a
more context-specific result without storing legally
demanded data in the general investigation data set. Both
supplementary and query-specific data has to be normalized
and matched to the objects and entities from the base data.
Fig. 1 shows the design of the software tool proposed in this
research work. In this research work a software tool has
been implemented which will be delivered to border
management agencies which will enhance the security
system at borders by discovering and revealing unusual
patterns of individuals and vehicles involved in criminal
activities. In addition to this, system enables sharing of
information among border agencies and local law
enforcement departments [1]. Thus it provides border
agencies a more efficient way to decide whether to allow
people or vehicle to cross the border or not.
For security concerns, this software tool provides an
authentic login id for border
agencies that ensures only
authentic users at the border can access the system.
This software provides border agencies with the database
that includes data collected and integrated from various
departments. This database is easily accessible by all
departments using their respective authentic login ids’
provided by the software. This database is dynamic in
nature since it gets updated every time any changes are
made by Police or investigative or Border security
departments.
Every time any person or vehicle crosses the border land,
Figure 1 System Design
C. Research Testbed
We integrated Police Department (PD) and Investigation
Department (ID) datasets with each other and with a dataset
57
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.
Border Security Up Gradation Using Data Mining
made available for this research by Border Security
Department.
The Border Security dataset was handled as a
supplementary source that identifies border crossing
vehicles and criminals.
1) Integrating the Data: Integration of the data sets
proceeded in three steps:
1. PD/ID records were mapped to a common schema.
2. Cross-jurisdictional identities were matched.
3. Border Security Agency data was imported as a
supplementary source.
Because the PD and ID data come from cooperative
agencies with closely related activities, it was appropriate to
invest in a significant integration effort. People were
matched on Passport Number. Each vehicle plate found in
the police records was matched to the Border Security
crossing data to establish a border crossing history and help
identify potentially interesting vehicles. We began
analyzing our data by evaluating the overlap between
datasets. We expect that future versions of our system will
allow inclusion of other supplemental and query-specific
data such as family associations, phone records, and jail
visitations. These sources could be added as part of an
interactive link-chart drawing process. We began analyzing
our data by evaluating the overlap between datasets.
2) CAN Evaluation: Next we measured the impact of crossjurisdictional information on activity networks traced in the
criminal activity records[2]. We randomly choose people
from a combined list of wanted suspects and known drug
traffickers. We selected only people appearing in both
Police and ID records (the large majority did appear in both
data sets). We used the associations or links that occur
when individuals or vehicles are listed together in an
incident report to trace CANs.
For each person we followed all known person-to-person
associations and compiled a list of people. Links for each
person in the new list were also followed. We then followed
person-to-vehicle links to identify plate numbers. The result
was a network of all people within two “hops” of the focus
individual and all associated vehicles known to have crossborder activity. We created three networks for each person:
one with links from the Police dataset, one with links found
in the ID dataset, and one using the links in both datasets.
This system reports the average number of associated
people, associated vehicles, and associational links found
for the selected individuals. Combining the data sets
allowed us to connect more people and border crossing
vehicles for this list of known criminals.
Next we created a set of CAN visualizations for review by
law enforcement personnel. We limited our networks to 50
nodes at most, because more nodes would overwhelm the
viewer (Experimentation helps establish appropriate initial
network sizes for display). While the size of a network may
“converge” quickly if little information is available,
networks frequently become unmanageable in just a few
iterations. A variety of visual cues were used in this
preliminary implementation. We differentiated entity types
by shape, key attributes by node color, degree of activity as
node size, connection source by link color, and some details
in link text or roll-over tool tips. Figure 2 shows a network
connecting narcotics traffickers and border crossing plates.
• Associations found in the Police Department data are blue,
ID links are green, and associations noted in both sets are
red.
• Node size indicates the extent of criminal activity. All
incidents involving a person are counted. Violent, narcoticsrelated, and gang-related activities are counted twice. The
activity scores are normalized to identify the relative
activity levels of the individuals in the network. Future
work will explore various methods of determining
appropriate node size.
• Nodes in the display are initially arranged by algorithm
which “pushes” and “pulls” nodes using the links in the
network. This algorithm needs further development but
generally tends to place closely related people near each
other in the display area.
Choice of these features was: high levels of criminal
activity and frequent border crossings signal useful
investigative leads, crime types and person roles are
important for association evaluation, and longer associative
paths are less interesting.
We implemented a system to enhance visualizations of
Police and ID data with border crossing information and
created networks of people and/or vehicles which all
included at least one vehicle with recorded border
crossings.
Figure 2 and 3 were analyzed and comments which are
summarized below.
These networks can be easily analyzed to reveal links
between relatively unknown subjects who are routinely
crossing the border and known participants in Police and ID
databases. The information generated by networks could
subsequently be used to focus and direct law enforcement
resources and investigations. Automatically generated
activity networks for wanted individuals would save a lot of
time and effort.
Indications of cross-border activity would be very useful in
focusing certain investigations. Correlating stolen vehicle
reports with border crossings and targeted individuals could
help in many investigations, but finding correlations
manually was very time consuming.
58
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.
International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-4, Issue-ICCIN-2K14, March 2014
ICCIN-2K14 | January 03-04, 2014
Bhagwan Parshuram Institute of Technology, New Delhi, India
Figure 2 Visualization of Criminal Activity Network
Figure 3 A complex network connecting border crossing plates and known drug trafficker
59
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.
Border Security Up Gradation Using Data Mining
make better operational decisions and the flow of
information back to local agencies helping them pursuing
related investigations. This software helps border check
post authorities to get more accurate, and relevant
information related to every vehicles and individual
crossing the border, thus helping border check post
authorities to more efficiently decide whether to allow a
vehicle or individual to cross border or not.
As we know that border and transportation security is very
important for country’s security, in the future work more
security features can be implemented. In the system
developed, information from law enforcement sources has
been utilized; hence in future information from different
countries can be collected and utilized to enhance the
security at borders.
IV. EXPERIMENTAL RESULT
A. Result based on information integration and sharing
While conducting the research it was found that integrating
the information from multiple sources and sharing it with
all the departments, helps in identifying many vehicles and
individuals crossing the border have some criminal incident
associated with them. At the border check posts the system
makes it easier to identify people/vehicle crossing with
criminal record, people having illegal driving license or
having illegal passport, vehicle with illegal registration,
stolen vehicles, vehicle carrying any contraband item. It is a
positive sign that this work will provide more relevant and
accurate information to the border check post authorities.
B. Result based on Fuzzy C- Mean algorithm
The Fuzzy C-Mean algorithm works by assigning
membership to each data point corresponding to each
cluster centre on the basis of distance between the cluster
centre and the data point. More the data is near to the
cluster centre, more is its membership towards the
particular cluster centre.
The system is able to draw criminal activity network
defining unknown relations among criminals and vehicles
involved and recorded for different criminal activities but
somehow linked with the same source of crime.
ACKNOWLEDGMENT
I would like to add few heartfelt words for the people who
were part of this project in numerous ways, people who
gave support in completing this report. Completion of the
project would not have been possible without mutual efforts
and integrated thoughts.
First of all I wish to express my gratitude and hearty
appreciation to my Project Supervisors and Mentors
Prof.S.S Prasad and Mrs. Sonali Mathur for providing a self
learning and congenial environment and for their
continuous encouragement and enthusiastic guidance in the
successful completion of the project work. They have been
instrumental in providing constant encouragement,
guidance and a stimulating and challenging environment in
which I have been welcomed, inspired and assisted.
I also extend my thanks to my internal panel members who
also gave their valuable advices on how to improve the
feasibility of the project during the viva-voce and
presentations. I would also like to thank other faculty
members and my colleagues here at JSSATE Noida for
providing me the optimal learning environment, which
enabled me to undertake this project.
C. Pattern analysis
The system is also able to evaluate a month wise crime rate
graph shown in figure 4. As per records entered till then
graph is showing more criminal activities at the border in
the months of May, June, July, August, September and
October whereas no criminal activities in rest of the months.
REFERENCES
[1]
[2]
Figure 4 Chart explaining criminal activities at borders each month
[3]
V. CONCLUSION AND FUTURE WORK
In the proposed system emphasis has been placed on
creating Criminal Activity Networks which are analyzed to
reveal links between relative obscure subjects who are
routinely crossing the border and known participants in
criminal activities recorded in Police and ID departments.
This system also helps border agents to plot a crime rate
graph so as to draw more attention on borders in months
having highest number of criminal behaviors.
The information obtained with the help of CAN and crime
rate graph could subsequently be used to focus and to direct
local law enforcements’ and border security agents’
resources and investigations. Automatically generated
activity networks for wanted individuals would save a lot of
time since finding correlations manually was very time
consuming.
We conclude that the system worked successfully on the
data that is used for the analysis. In this software integration
and sharing of information has been encouraged to draw
useful and unknown links between criminals and vehicles
as compared to previous research work. It increases the
information flow to the border agencies, allowing them to
[4]
[5]
[6]
[7]
[8]
[9]
[10]
60
S. Kaza, P. J.-H. Hu, H.-F. Hu, H. Chen. "Designing,
Implementing, and Evaluating Information Systems for Law
Enforcement—A Long-Term Design-Science Research Project,"
Communications of the AIS, Volume 29, Issue 1, 2011.
Chau, M., Schroeder, J., Xu, J., Chen, H., "Automated Criminal
Link Analysis Based on Domain Knowledge," Journal of the
American Society for Information Science and Technology,
Volume 58, Number 6, Pages 842-855, 2007.
S. Kaza, Y. Wang, and H. Chen, "Enhancing Border Security:
Mutual Information Analysis to Identify Suspect Vehicles,"
Decision Support Systems, Volume 43, Number 1, Pages 199-210,
2007.
Sinchun chen, fei-yue wang, Daniel zeng, “Intelligence and security
informatics for homeland security:information, communication, and
transportation”, IEEE transactions on intelligent transportation
systems vol.5,no.4, 2004.
Byron Marshall, Siddharth Kaza, Jennifer Xu, Homa Atabakhsh,
Tim Petersen, Chuck Violette, and Hsinchun Chen, “cross
jurisdictional crime activity networks to support border and
transportation security”, 2004.
Siddharth kaza, Tao wag, Hemanth Gowda, Hsinchun chen, “Target
vehicle identification for border safety using mutual information” in
proc. of 8th international conference on intelligent transportation
system Vienna, Austrias, 2005.
Jiawei Han and Micheline kamber, “Data Mining: Concepts and
Techniques”, Second edition, Morgan Kaufmann, 2006.
Anil K. Jain and Richard C. Dubes, “Algorithms for Clustering
Data”, Prentice Hall, 1988.
A. Baraldi, and P. Blonda, "A survey of fuzzy clustering algorithms
for pattern recognition” IEEE Transactions on Systems, Man and
Cybernetics, Part B (Cybernetics), 1998.
Frank Hoppner, Frank Klawonn, Rudolf Kruse, and Thomas
Runkler, “Fuzzy Cluster Analysis: Methods for Classification, Data
Analysis, and Image Recognition”, John Wiley & Sons Ltd.,
Chinchester New York Weinheim, 1999.
Published By:
Blue Eyes Intelligence Engineering
& Sciences Publication Pvt. Ltd.