Group Members :Amit kumar

GokulahasanNishanthiRajkumar

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 intelligence-

gathering 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 crimesExtracting named entities from

narrative reportsDetecting deceptive criminal identitiesIdentifying 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 frameworkIdentifies 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 extractionAssociation 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 ApplicationEntity 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