Crime Data Mining - Case Study
This document was uploaded by user and they confirmed that they have the permission to share it. If you are author or own the copyright of this book, please report to us by using this DMCA report form. Report DMCA
Overview
Download & View Crime Data Mining - Case Study as PDF for free.
More details
- Words: 1,256
- Pages: 33
Group Members : Amit kumar Gokulahasan Nishanthi Rajkumar
What is a crime?
The breach of one or more rules or laws for which governing authority via police power may ultimately prescribe a conviction. It is injurious to the general population or the state. So, crime prevention and identifying the criminals is the necessity in today's society.
Major challenges
All law-enforcement and intelligencegathering organizations are currently facing problems of accurately and efficiently analyzing the growing volumes of crime data Different modes, patterns, cross-border operations, technologically advanced crimes are difficult to track and solve the case. Investigation of the crime takes longer duration due to complexity of issues.
What is data mining? “Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so that they may be used in an enterprise’s decision making process.”
Advantages of data mining
Lot of permutations & combinations can be incorporated in the software Less time consuming and better accuracy Installing and running the software costs much less than hiring personnel Different data mining techniques or combination of some can be incorporated in one assignment Advancement in data mining field is yielding better and better results
Which model suits the process of criminal identification? Different law-enforcement agencies are involved in
investigation of different kinds based on severity and jurisdiction of crime. Researchers have developed various automated data mining techniques for both local law enforcement and national security applications.
Objective of Crime Data Mining: Using Data mining techniques to aid
analysis of data related to crimes Extracting named entities from narrative reports Detecting deceptive criminal identities Identifying criminal groups and key members
Entity extraction used to automatically identify persons, addresses, vehicles, and personal characteristics from police narrative reports subsequently helps in grouping similar activities by criminals and tracing their behavior Its performance depends greatly on the availability of extensive amounts of clean input data.
Clustering techniques group data items into classes with similar characteristics to
maximize or minimize intraclass similarity use the statistics-based concept space algorithm to automatically associate different objects such as persons, organizations, and vehicles in crime records link analysis techniques to identify similar transactions It can automate a major part of crime analysis but is limited by the high computational intensity typically required
Association rule mining discovers frequently occurring item sets in a database and
presents the patterns as rules application in network intrusion detection to derive association rules from users’ interaction history, detection of intruders’ profiles to help detect potential future network attacks. Similar to this sequential pattern mining can be applied to find patterns. Performance of these techniques relies on the accuracy and richness of available data .
Deviation detection Used to overcome the deviation in the data produced by
the criminals so it’s also called outlier detection. Applicable in fraud detection, network intrusion detection, and other crime analyses But identifying the incorrect data is itself a tedious job.
Classification finds common properties among different crime entities
and organizes them into predefined classes Applicable in identify the source of e-mail spamming based on the sender’s linguistic patterns and structural features used to predict crime trends, classification can reduce the time required to identify crime entities Performance is dependent on richness of data
String comparator compares the textual fields in pairs of database records and
compute the similarity between the records applicable in detect deceptive information such as name, address, and Social Security number in criminal records
Social network analysis Explains the roles of and interactions among nodes in a
conceptual network Used to construct a network that illustrates criminals’ roles, the flow of tangible and intangible goods and information, and associations among these entities In-depth analysis can reveal critical roles and subgroups and vulnerabilities inside the network
Caution Entity Extraction & Sequential Pattern Mining –
requires rich data for accuracy Clustering Techniques & String comparator – High computational intensity Deviation Detection – Appear to be normal Classification – Predefined classification scheme Social Networking – Low profile
Crime data mining framework Identifies relationships between techniques applied in criminal and intelligence analysis at various levels
Case 1: Named Entity Extraction 36 narcotics related cases-AI entity extractor 3 steps
-identifies noun phrases -calculates a set of feature scores for phrases -predicts the most likely entity type Entities- names, addresses, vehicles, narcotics names, physical characteristics
Case 2: Deceptive entity detection Criminals provide false information about themselves This creates redundancy in the database Makes probing into further details about them,
difficult
An Alternative Analysis Other techniques that
can be utilized
Entity extraction Association rule mining
combined with outlier detection
Criminal- Network Analysis Problems: Drug, Cybercrime, Terrorism etc. Clue: Criminals often develops networks in which they
form groups or teams to carry out various illegal activites.
Objective Primery: To identify subgroups and key members in
the criminal networks and studying interaction patterns Secondary: To develop effective strategies for disrupting the networks
Data Collection 272 Tucson Police Department incidents summaries Involving 164 crimes Committed form 1985 through May 2002
Methods and Techniques Concept Space (Clustering)
To extract criminal relations and create a likely network of suspects. Co – Occurrence Weight – To find the strength Hierarchical Clustering – To partition the network into subgroups Block Modeling – To identify interaction patterns between these subgroups
For Key Member…. Centrality measures Degree Betweenness Closeness
164 Criminals
Sub Groups
Validation 2 hr field study with 3 Tucson Police Department domain experts Evaluated the analysis’s validity The analysis was valid.
Advantages Increase crime analysts’ work productivity Visualize Criminal Networks Risk is reduced Time is saved-Police can use it for other valuable tasks Reduce error Effective strategies can be formulated to disrupting
criminal networks
PS : Only Static Network is visualized
Emerging Field Techniques
Application
Entity Extraction
To analyze the behavioral pattern of serial offenders
Crime Association & Clustering
Reveals the identities of cyber-criminals who use the internet to spread illegal messages or malicious code
Machine Learning Algorithms • ID3 • Neural Networks • Support Vector Machine • Genetic Algorithms
To predict crimes by analyzing factors such as Time, Location, Vehicle, Address. Physical characteristics, and Property
Applying the technique Using Entity Extraction Recognizing a pattern of deception Using Association Rule Arriving at a general rule for a deceptive entry Using Outlier Detection Spotting the odd profile
Approach adopted in the case study: They have adopted an experimental analysis and a
little bit of simulation and they have interpreted from the conclusions there from They have explored the system of analysis by trying to solve the problems using newer methods and approaches of data mining
Conclusion: Crime data has increased to very large quantities running into zota bytes(1024 bytes) requiring advanced techniques such as data mining Data mining has immense potential for crime data analysis As is the case with any other new technology, even DM has its own limitations as of now But as the technology advances, it is going to be one of the most powerful tools of data analysis
What is a crime?
The breach of one or more rules or laws for which governing authority via police power may ultimately prescribe a conviction. It is injurious to the general population or the state. So, crime prevention and identifying the criminals is the necessity in today's society.
Major challenges
All law-enforcement and intelligencegathering organizations are currently facing problems of accurately and efficiently analyzing the growing volumes of crime data Different modes, patterns, cross-border operations, technologically advanced crimes are difficult to track and solve the case. Investigation of the crime takes longer duration due to complexity of issues.
What is data mining? “Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. The patterns must be actionable so that they may be used in an enterprise’s decision making process.”
Advantages of data mining
Lot of permutations & combinations can be incorporated in the software Less time consuming and better accuracy Installing and running the software costs much less than hiring personnel Different data mining techniques or combination of some can be incorporated in one assignment Advancement in data mining field is yielding better and better results
Which model suits the process of criminal identification? Different law-enforcement agencies are involved in
investigation of different kinds based on severity and jurisdiction of crime. Researchers have developed various automated data mining techniques for both local law enforcement and national security applications.
Objective of Crime Data Mining: Using Data mining techniques to aid
analysis of data related to crimes Extracting named entities from narrative reports Detecting deceptive criminal identities Identifying criminal groups and key members
Entity extraction used to automatically identify persons, addresses, vehicles, and personal characteristics from police narrative reports subsequently helps in grouping similar activities by criminals and tracing their behavior Its performance depends greatly on the availability of extensive amounts of clean input data.
Clustering techniques group data items into classes with similar characteristics to
maximize or minimize intraclass similarity use the statistics-based concept space algorithm to automatically associate different objects such as persons, organizations, and vehicles in crime records link analysis techniques to identify similar transactions It can automate a major part of crime analysis but is limited by the high computational intensity typically required
Association rule mining discovers frequently occurring item sets in a database and
presents the patterns as rules application in network intrusion detection to derive association rules from users’ interaction history, detection of intruders’ profiles to help detect potential future network attacks. Similar to this sequential pattern mining can be applied to find patterns. Performance of these techniques relies on the accuracy and richness of available data .
Deviation detection Used to overcome the deviation in the data produced by
the criminals so it’s also called outlier detection. Applicable in fraud detection, network intrusion detection, and other crime analyses But identifying the incorrect data is itself a tedious job.
Classification finds common properties among different crime entities
and organizes them into predefined classes Applicable in identify the source of e-mail spamming based on the sender’s linguistic patterns and structural features used to predict crime trends, classification can reduce the time required to identify crime entities Performance is dependent on richness of data
String comparator compares the textual fields in pairs of database records and
compute the similarity between the records applicable in detect deceptive information such as name, address, and Social Security number in criminal records
Social network analysis Explains the roles of and interactions among nodes in a
conceptual network Used to construct a network that illustrates criminals’ roles, the flow of tangible and intangible goods and information, and associations among these entities In-depth analysis can reveal critical roles and subgroups and vulnerabilities inside the network
Caution Entity Extraction & Sequential Pattern Mining –
requires rich data for accuracy Clustering Techniques & String comparator – High computational intensity Deviation Detection – Appear to be normal Classification – Predefined classification scheme Social Networking – Low profile
Crime data mining framework Identifies relationships between techniques applied in criminal and intelligence analysis at various levels
Case 1: Named Entity Extraction 36 narcotics related cases-AI entity extractor 3 steps
-identifies noun phrases -calculates a set of feature scores for phrases -predicts the most likely entity type Entities- names, addresses, vehicles, narcotics names, physical characteristics
Case 2: Deceptive entity detection Criminals provide false information about themselves This creates redundancy in the database Makes probing into further details about them,
difficult
An Alternative Analysis Other techniques that
can be utilized
Entity extraction Association rule mining
combined with outlier detection
Criminal- Network Analysis Problems: Drug, Cybercrime, Terrorism etc. Clue: Criminals often develops networks in which they
form groups or teams to carry out various illegal activites.
Objective Primery: To identify subgroups and key members in
the criminal networks and studying interaction patterns Secondary: To develop effective strategies for disrupting the networks
Data Collection 272 Tucson Police Department incidents summaries Involving 164 crimes Committed form 1985 through May 2002
Methods and Techniques Concept Space (Clustering)
To extract criminal relations and create a likely network of suspects. Co – Occurrence Weight – To find the strength Hierarchical Clustering – To partition the network into subgroups Block Modeling – To identify interaction patterns between these subgroups
For Key Member…. Centrality measures Degree Betweenness Closeness
164 Criminals
Sub Groups
Validation 2 hr field study with 3 Tucson Police Department domain experts Evaluated the analysis’s validity The analysis was valid.
Advantages Increase crime analysts’ work productivity Visualize Criminal Networks Risk is reduced Time is saved-Police can use it for other valuable tasks Reduce error Effective strategies can be formulated to disrupting
criminal networks
PS : Only Static Network is visualized
Emerging Field Techniques
Application
Entity Extraction
To analyze the behavioral pattern of serial offenders
Crime Association & Clustering
Reveals the identities of cyber-criminals who use the internet to spread illegal messages or malicious code
Machine Learning Algorithms • ID3 • Neural Networks • Support Vector Machine • Genetic Algorithms
To predict crimes by analyzing factors such as Time, Location, Vehicle, Address. Physical characteristics, and Property
Applying the technique Using Entity Extraction Recognizing a pattern of deception Using Association Rule Arriving at a general rule for a deceptive entry Using Outlier Detection Spotting the odd profile
Approach adopted in the case study: They have adopted an experimental analysis and a
little bit of simulation and they have interpreted from the conclusions there from They have explored the system of analysis by trying to solve the problems using newer methods and approaches of data mining
Conclusion: Crime data has increased to very large quantities running into zota bytes(1024 bytes) requiring advanced techniques such as data mining Data mining has immense potential for crime data analysis As is the case with any other new technology, even DM has its own limitations as of now But as the technology advances, it is going to be one of the most powerful tools of data analysis
Related Documents
Crime Data Mining - Case Study
May 2020 1
Data Mining
May 2020 23
Data Mining
October 2019 35
Data Mining
November 2019 32
Data Mining
May 2020 21