Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
As per studies conducted by the University of California, it is observed that crime in any area follows the same pattern as that of earthquake aftershocks. It is difficult to predict an earthquake, but once it happens the aftershocks following it are quite predictable. Same is true for the crimes happening in a geographical area.
Crime Analysis & Prediction System is a system to analyze & detect crime hotspots & predict crime.
It collects data from various data sources - crime data from OpenData sites, US census data, social media, traffic & weather data etc.
It leverages Microsoft's Azure Cloud and on premise technologies for back-end processing & desktop based visualization tools.
Crime Analytics: Analysis of crimes through news paper articlesChamath Sajeewa
Crime analysis is one of the most important
activities of the majority of the intelligent and law enforcement
organizations all over the world. Generally they collect domestic
and foreign crime related data (intelligence) to prevent future
attacks and utilize a limited number of law enforcement
resources in an optimum manner. A major challenge faced by
most of the law enforcement and intelligence organizations is
efficiently and accurately analyzing the growing volumes of crime
related data. The vast geographical diversity and the complexity
of crime patterns have made the analyzing and recording of
crime data more difficult. Data mining is a powerful tool that can
be used effectively for analyzing large databases and deriving
important analytical results. This paper presents an intelligent
crime analysis system which is designed to overcome the above
mentioned problems. The proposed system is a web-based system
which comprises of crime analysis techniques such as hotspot
detection, crime comparison and crime pattern visualization. The
proposed system consists of a rich and simplified environment
that can be used effectively for processes of crime analysis.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Crime rate analysis using k nn in python
PredPol: How Predictive Policing WorksPredPol, Inc
PredPol’s cloud-based predictive policing software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.
PredPol’s technology has been helping law enforcement agencies to dramatically reduce crime in jurisdictions of all types and sizes, across the U.S. and overseas. Over the past year, Atlanta and Los Angeles have reduced specific crimes in targeted areas at rates ranging from nearly 20% to over 40%. Smaller jurisdictions, such as Norcross, Georgia, have seen nearly a 30% reduction in burglaries and robberies; in Alhambra, California, car burglaries have dropped 20% since the software technology was deployed.
Using advanced mathematics and computer learning, PredPol’s algorithms predict many types of crime, including property crimes, drug incidents, gang activity, and gun violence as well as traffic accidents.
Only three pieces of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions.
Crime analysts and command staff using PredPol are 100% more effective than they are with traditional hotspot mapping at predicting where and when crimes are likely to occur. That means police have twice as many opportunities to deter and reduce crime.
As per studies conducted by the University of California, it is observed that crime in any area follows the same pattern as that of earthquake aftershocks. It is difficult to predict an earthquake, but once it happens the aftershocks following it are quite predictable. Same is true for the crimes happening in a geographical area.
Crime Analysis & Prediction System is a system to analyze & detect crime hotspots & predict crime.
It collects data from various data sources - crime data from OpenData sites, US census data, social media, traffic & weather data etc.
It leverages Microsoft's Azure Cloud and on premise technologies for back-end processing & desktop based visualization tools.
Crime Analytics: Analysis of crimes through news paper articlesChamath Sajeewa
Crime analysis is one of the most important
activities of the majority of the intelligent and law enforcement
organizations all over the world. Generally they collect domestic
and foreign crime related data (intelligence) to prevent future
attacks and utilize a limited number of law enforcement
resources in an optimum manner. A major challenge faced by
most of the law enforcement and intelligence organizations is
efficiently and accurately analyzing the growing volumes of crime
related data. The vast geographical diversity and the complexity
of crime patterns have made the analyzing and recording of
crime data more difficult. Data mining is a powerful tool that can
be used effectively for analyzing large databases and deriving
important analytical results. This paper presents an intelligent
crime analysis system which is designed to overcome the above
mentioned problems. The proposed system is a web-based system
which comprises of crime analysis techniques such as hotspot
detection, crime comparison and crime pattern visualization. The
proposed system consists of a rich and simplified environment
that can be used effectively for processes of crime analysis.
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Crime rate analysis using k nn in python
PredPol: How Predictive Policing WorksPredPol, Inc
PredPol’s cloud-based predictive policing software enables law enforcement agencies to better prevent crime in their communities by generating predictions on the places and times that future crimes are most likely to occur.
PredPol’s technology has been helping law enforcement agencies to dramatically reduce crime in jurisdictions of all types and sizes, across the U.S. and overseas. Over the past year, Atlanta and Los Angeles have reduced specific crimes in targeted areas at rates ranging from nearly 20% to over 40%. Smaller jurisdictions, such as Norcross, Georgia, have seen nearly a 30% reduction in burglaries and robberies; in Alhambra, California, car burglaries have dropped 20% since the software technology was deployed.
Using advanced mathematics and computer learning, PredPol’s algorithms predict many types of crime, including property crimes, drug incidents, gang activity, and gun violence as well as traffic accidents.
Only three pieces of data are used to make predictions – type of crime, place of crime, and time of crime. No personal data is utilized in making these predictions.
Crime analysts and command staff using PredPol are 100% more effective than they are with traditional hotspot mapping at predicting where and when crimes are likely to occur. That means police have twice as many opportunities to deter and reduce crime.
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
Crime sensing with big data - Singapore perspectiveBenjamin Ang
This presentation examines the Potentials and Limitations of Using Big Data for Crime Estimation. Singapore laws discussed include Personal Data Protection Act (PDPA), Penal Code, Criminal Procedure Code (CPC). Topics covered include Crime Analysis, Crime Prediction, Algorithm Bias, and other risks. The video of this presentation can be found at https://youtu.be/kctB3lRLh2U
Our goal is to create a web application that would give insights to its user about the crime scenario and its various aspects in Chicago.
Our application will contain:
A search box/drop down list where user can select a district.
Geospatial analysis using ArcGIS maps and visualizations that are embedded into the web app which will be dynamically updated to show most interesting patterns or heat maps for that district.
Statistical analysis and visualizations on historical data to the user.
Prediction of the date when the next crime will happen and its probability.
Intelligence Led Policing for Police Decision MakersDeborah Osborne
Intelligence-Led Policing for Decision-Makers Webinar
Audio is at http://www.blogtalkradio.com/Deborah-Osborne/2009/09/23/Intelligence-Led-Policing-for-Decision-Makers-Webinar
This webinar, designed for law enforcement managers, covers the following topics:
* Intelligence: what it is, what it is not, and what it can be
* The role of the decision-maker in the intelligence cycle
* Defining Intelligence-Led Policing and the 3 i's cycle
* The 7 stages of Intelligence-Led Policing
* Resources for learning more about Intelligence-Led Policing
Malware Dectection Using Machine learningShubham Dubey
Malware detection is an important factor in the security of the computer systems. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. That is why the need for machine learning-based detection arises.
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
I downloaded data from from City of Chicago Data Portal and made the analysis of 2014 Crime Data. This is just a simple version. I can do more complicated analysis if needed. I used Excel to do this analysis.
- Project Title: Chicago crime analysis
- Course name: Principles and Practice in Data Mining
- Semester: Autumn 2016
- Professor: Yuran SEO
- Sungkyunkwan University
- Department: philosophy
- Name: jangyoung seo
- Contact: laiha10@naver.com
Crime Data Analysis, Visualization and Prediction using Data MiningAnavadya Shibu
This paper presents a general idea about the
model of Data Mining techniques and diverse crimes. It also
provides an inclusive survey of competent and valuable
techniques on data mining for crime data analysis. The
objective of the data mining is to recognize patterns in
criminal manners in order to predict crime anticipate
criminal activity and prevent it. This project implements a
novel data mining techniques like KNN, Text Clustering, IR
tree for investigating the crime data sets and sorts out the
accessible problems. The collective knowledge of various
data mining algorithms tend certainly to afford an
enhanced, incorporated, and precise result over the crime
prediction in the banking sectors Our law enforcement
organizations require to be adequately outfitted to defeat
and prevent the crime. This project is developed using Java
as front-end and MySQL as back-end. Supporting
applications like Sunset, NetBeans are used to make the
portal more interactive.
Crime sensing with big data - Singapore perspectiveBenjamin Ang
This presentation examines the Potentials and Limitations of Using Big Data for Crime Estimation. Singapore laws discussed include Personal Data Protection Act (PDPA), Penal Code, Criminal Procedure Code (CPC). Topics covered include Crime Analysis, Crime Prediction, Algorithm Bias, and other risks. The video of this presentation can be found at https://youtu.be/kctB3lRLh2U
Our goal is to create a web application that would give insights to its user about the crime scenario and its various aspects in Chicago.
Our application will contain:
A search box/drop down list where user can select a district.
Geospatial analysis using ArcGIS maps and visualizations that are embedded into the web app which will be dynamically updated to show most interesting patterns or heat maps for that district.
Statistical analysis and visualizations on historical data to the user.
Prediction of the date when the next crime will happen and its probability.
Intelligence Led Policing for Police Decision MakersDeborah Osborne
Intelligence-Led Policing for Decision-Makers Webinar
Audio is at http://www.blogtalkradio.com/Deborah-Osborne/2009/09/23/Intelligence-Led-Policing-for-Decision-Makers-Webinar
This webinar, designed for law enforcement managers, covers the following topics:
* Intelligence: what it is, what it is not, and what it can be
* The role of the decision-maker in the intelligence cycle
* Defining Intelligence-Led Policing and the 3 i's cycle
* The 7 stages of Intelligence-Led Policing
* Resources for learning more about Intelligence-Led Policing
Malware Dectection Using Machine learningShubham Dubey
Malware detection is an important factor in the security of the computer systems. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. That is why the need for machine learning-based detection arises.
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
I downloaded data from from City of Chicago Data Portal and made the analysis of 2014 Crime Data. This is just a simple version. I can do more complicated analysis if needed. I used Excel to do this analysis.
- Project Title: Chicago crime analysis
- Course name: Principles and Practice in Data Mining
- Semester: Autumn 2016
- Professor: Yuran SEO
- Sungkyunkwan University
- Department: philosophy
- Name: jangyoung seo
- Contact: laiha10@naver.com
A Comparative Study of Data Mining Methods to Analyzing Libyan National Crime...Zakaria Zubi
Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
Crime Mapping & Analysis – Georgia Tech
Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Information on patterns can help law enforcement agencies deploy resources in a more effective manner, and assist detectives in identifying and apprehending suspects.
In developing countries, the lack of infrastructure like GPS (Global positioning system) and GIS (Geographic Information system) have hindered the growth of the police department. This paper proposes a simple, useful and cost effective solution for crime mapping. Google cloud resources like satellite data, application and GIS software have been used to develop this application. The developer requires only a simple computer connected to the internet. The source of crime data is the RSS (Really Simple Syndication) feeds from various news websites.
SUPERVISED AND UNSUPERVISED MACHINE LEARNING METHODOLOGIES FOR CRIME PATTERN ...ijaia
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern ...gerogepatton
Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big
impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of
supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime
analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting
and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization
strategies. Further, we use a graph based unsupervised machine learning technique called core periphery
structures to analyze how crime behavior evolves over time. These methods can be generalized to use for
different counties and can be greatly helpful in planning police task forces for law enforcement and crime
prevention.
Data mining and machine learning have become a vital part of crime detection and prevention. In this
research, we use WEKA, an open source data mining software, to conduct a comparative study between the
violent crime patterns from the Communities and Crime Unnormalized Dataset provided by the University
of California-Irvine repository and actual crime statistical data for the state of Mississippi that has been
provided by neighborhoodscout.com. We implemented the Linear Regression, Additive Regression, and
Decision Stump algorithms using the same finite set of features, on the Communities and Crime Dataset.
Overall, the linear regression algorithm performed the best among the three selected algorithms. The scope
of this project is to prove how effective and accurate the machine learning algorithms used in data mining
analysis can be at predicting violent crime patterns.
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
A crime is an act which is against the laws of a country or region. The technique which is used to find areas on a map which have high crime intensity is known as crime hotspot prediction. The technique uses the crime data which includes the area with crime rate and predict the future location with high crime intensity. The motivation of crime hotspot prediction is to raise people’s awareness regarding the dangerous location in certain time period. It can help for police resource allocation for creating a safe environment. The paper presents survey of different types of data mining techniques for crime hotspots prediction.
Physical and Cyber Crime Detection using Digital Forensic Approach: A Complet...IJARIIT
Criminalization may be a general development that has significantly extended in previous few years. In
order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis
is a section records in data mining plays a crucial role in terms of predicting and learning the criminals. In our
paper, we've got planned an incorporated version for physical crime as well as cybercrime analysis. Our approach
uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools
(DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool
(CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process
Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as physical crime
or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis we've used kmeans
approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the
utilization of GMAPI generation. This provides advanced and consumer-friendly visual-aid to k-means approach for
tracing the region of the crime. we have applied KNN for criminal identification with the
help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then
the new crime sample are introduced to the crime data-set. With the advancements of web, the network form has
become much more complicated and attacking methods are further more than that as well. For crime analysis
we're detecting the attacks executed on host system through an outsider the usage of
assorted digitized forensic tools to produce information security with the help of generating reports for an
event which could need any investigation. Our digitized technique aids the development of the society
by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal
identification as opposed to manually looking the database to analyze criminal activities, and as a
result facilitate them in combating crimes.
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Using Data Mining Techniques to Analyze Crime Pattern
1. “ Add your company slogan ”
Using Data Mining
Techniques to Analyze Crime
Pattern
Presented by:
Dr.Zakaria Suliman Zubi
Associate Professor
Computer Science Department
Faculty of Science
Sirte University
Sirte, Libya
LOGO
3. Abstract
Law enforcement agencies represented in the police today faced a
large volume of data every day. These data can be processed and
transformed into useful information. In this since, Data mining can
be applied to greatly improve crime analysis. Which can help to
reduce and preventing crime as much as possible.
Crime reports and data are used as an input for the formulation of
the crime prevention policies and strategic plans.
This work will apply some data mining methods to analyses Libyan
national criminal record data to help the Libyan government to make
a strategically decision regarding prevention the increasing of the
high crime rate these days.
The data was collected manually from Benghazi, Tripoli, and AlJafara Supremes Security Committee (SSC).
Our proposed model will be able to extract crime patterns by using
association rule mining and clustering to classify crime records on
the basis of the values of crime attributes.
www.themegallery.com
5. Introduction
Data Mining or Knowledge Discovery in Databases (KDD) in simple
words is nontrivial extraction of implicit, previously unknown, and
potentially useful information from data.
KDD is the process of identifying a valid, potentially, useful and
ultimately understandable structure in data..
Crime analyzes is an emerging field in law enforcement without
standard definitions. This makes it difficult to determine the crime
analyzes focus for agencies that are new to the field.
Crime analysis is act of analyzing crime. More specifically, crime
analysis is the breaking up of acts committed in violation of laws into
their parts to find out their nature and reporting, some analysis.
The role of the crime analysts varies from agency to agency.
Statement of these findings, The objective of most crime analysis is
to find meaningful information in vast amounts of data and
disseminate this information to officers and investigators in the field
to assist in their efforts to apprehend criminals and suppress
criminal activity.
www.themegallery.com
7. Data Mining Models
The Data Mining models are categorized into different leaves. Further,
each leaf signifies the relationship, if any, that is highlighted from the
database. the Data Mining Models can be put into one of the six main
categories: 1) Association, 2) Classification, 3) Clustering, 4)
Prediction, 5) Sequence Discovery, and 6) Generalization
www.themegallery.com
8. data Mining Models Cont…
Table 2 classifies the various Data Mining algorithms
according to problem type, namely, Association,
Classification, Clustering, Prediction, Discovery, and
Summarization.
www.themegallery.com
11. Why Analyze Crime?
Crime Analysts usually tend to justify their existence as crime analysts in what is
known as law enforcement agency. it makes sense to analyze crime. Some good
reasons are listed as follow:
1. Analyze crime to inform law enforcers about general and specific crime
trends, patterns, and series in an ongoing, timely manner.
2. Analyze crime to take advantage of the abundance of information existing in
law enforcement agencies, the criminal justice system, and public domain.
3. Analyze crime to maximize the use of limited law enforcement resources.
4. Analyze crime to have an objective means to access crime problems locally,
regionally, nationally within and between law enforcement agencies.
5. Analyze crime to be proactive in detecting and preventing crime.
6. Analyze crime to meet the law enforcement needs of a changing society.
www.themegallery.com
12. Why Analyze Crime? Cont…
In general there are four different techniques for
analyzing crimes, as follow:
1.
2.
3.
4.
Linkage Analysis
Statistical Analysis
Profiling
Spatial Analysis
Each of the above techniques has its own advantages
and drawbacks and can be used in specific cases.
www.themegallery.com
14. Data Mining Task
A. Data collection.
The dataset that was used as training and testing data
set were extracted from the Supreme Security
Committee in Tripoli, Benghazi and Al-Jafara.
These data contain data about both Crimes and
Criminals with the following main attributes:
1. Crime ID: Individual crimes are designated by unique crime id.
2. Crime type: indicates crime type.
3. Date: Indicate when a crime happened.
4. Gender: Male or Female.
5. Age: age of individual Criminal.
6. Crime Address: location of the crime.
7. Marital status: status of the Criminal.
www.themegallery.com
15. Data Mining Task Cont…
B. Data Preprocessing .
Real world usually have the following drawbacks:
Incompleteness, Noisy, and Inconsistence. So these data need to
be preprocessed to get the data suitable for analysis purpose. The
preprocessing includes the following tasks as it shown in
1.
2.
3.
4.
5.
Data cleaning.
Data integration
Data transformation.
Data reduction.
Data discretization
Figure (2) shows the distribution of
offenses versus different crime and
criminal attributes.
Figure (2): attributes for crime and criminal
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17. Data Set
We will consider crime database as a training dataset used in
our model. The mentioned database contains a real data values
from crime and criminal attributes. We will also consider 70
percent as training value of the proposed model and 30 percent
for testing. The following table shows the data we used in our
model.
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19. The MLCR proposed model
The Mining Libyan Criminal Record (MLCR) proposed
model will be implemented to conduct and interact with two
types of mining algorithms to overcome with two different
types of results effectively. Those two approaches are
considered as a sub-prototypes of the proposed MLCR
model. Those prototypes will be illustrated as follows:
A.Mining Libyan Criminal Record-using Association rules
(MLCR-AR).
B.Mining Libyan Criminal Record-using Clustering (MLCR-C).
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20. The MLCR proposed model Cont…
A. Mining Libyan Criminal Record-using
Association rules (MLCR-AR).
Association rule mining is a method used to generate rules from crime
dataset based on frequents occurrence of patterns to help the decision
makers of our security society to make a prevention action.
One of the most popular algorithm are called Apriori and FP-growth
Association rule mining classically intends at discovering association
between items in a transactional database.
The Apriori algorithm called also as “Sequential Algorithm” developed
by [Agrawal1994]. Is a great accomplishment in the history of mining
association rules[Cheung1996c]. It is also the most well known
association rules algorithm. This technique uses to perform association
analyze on the attributes of crimes.
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21. The MLCR proposed model Cont…
B. Mining Libyan Criminal Record-using Clustering (MLCR-C).
This prototype will use the same dataset indicated in MLCR_AR prototype. But
with Clustering Analysis.
Clustering is the technique that is used to group objects (crime and criminals)
without having predefined specification for their attributes.
Clustering is unsupervised classification: no predefined classes. Simple K-means
clustering algorithm is used in this work.
K-mean algorithm clusters the data members groups were m is predefined. InputCrime type. Number of clusters, Number of Iteration Initial seeds might produce
an important role in the final results.
Step1: Randomly choose cluster centers.
Step2: Assign instance to cluster based on their Distance to the cluster centers.
Step3: Centers of clusters are adjusted.
Step4: go to Step1 until convergence.
Step5: output X0 ,X1,X2 ,X3.
Fig3: criminal age vs. crime type After applying K-means algorithm
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23. Conclusion
Clustering and association rules were defined as a data mining techniques to
automatically retrieve, extract and evaluate information for knowledge
discovery from crime data.
This information was collected from many police departments in Libya.
Association rules Mining is one of the data mining techniques for data to be
used to identify the relationship and to generate rules from crime dataset based
on frequents occurrence of patterns to help the decision makers of our security
society to make a prevention action.
Clustering is one of the data mining techniques also used to group objects
(crime and criminals) without having predefined specification for their
attributes.
The algorithms such as K-means algorithm and Aproir algorithm are used in
this paper.
Those algorithms were expressed in details and a comparative study were
denoted in this paper.
A promising results were shown in the following figure.
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