1. Adapting and Visualizing Association Rule Mining Systems
for Law Enforcement Purposes
T.K. Cocx,
4/14/2019
W. Kosters

2. Research Area Criminal Career Study Computer Science Sociology
Goals
Practice
Structure
Difficulties
Algorithm
Results
Problems
Computer
Science
Sociology
Criminal Career Study
Psychology
Criminology
Law
4/14/2019
T.K. Cocx,

3. Criminal Careers
4/14/2019
T.K. Cocx,

4. Analysis Goal
Analysis
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T.K. Cocx,

5. Different angles to solution
Analyze criminal records  predict career OR
Understand crime better by (automatically) analyzing its characteristics.
Both solutions work on same database
National crime record database
Done by de Bruin et al. (ICDM 2006)
Focus of this talk
4/14/2019
T.K. Cocx,

6. Focus Database contains both crime AND demographic data
Relations between occurrences of certain crimes within individual careers
Teaches crimes that predict others in a more general case
Alarms
Relations between crimes and demographic data within individual careers
Discovers problematic ‘configurations’ within demographic areas
Better deploy police work forces
4/14/2019
T.K. Cocx,

7. Approach Employ association rule mining:
Use standard Apriori methods
‘Common’  overpresent (statistically)
Find common subsets of attributes
A subset can only be common if all its subgroups are common as well
Create tree containing all rules
Visualize tree to police end user
Is data suitable for this method?
4/14/2019
T.K. Cocx,

8. Problems Data is not boolean.
Nature of database (or crime itself) is responsible for a large number of over- or under-present attributes.
Male (80%)
Dutch (90%)
Addiction indication (4%)
Inherit relation between certain attributes pollutes outcome.
Semi-aggregated attributes (first age / last age for one-timers)
Descend / born
4/14/2019
T.K. Cocx,

9. Approach (Cont.) Fivethold method Database refit Attribute ban
Semantic Split
Interestingness
Tree visualization
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T.K. Cocx,

10. Database refit Standard methods rely on boolean databases
Attributes are present, or not.
Numerical attributes are discretized
Age  10 year intervals
Nominal attributes are split into all available categories
Resulting database is boolean
Very large (all different etniticities)
Very sparse (only one of them true)
4/14/2019
T.K. Cocx,

11. Attribute ban The database consists of many over-present attributes
Often lacking descriptive value (shopping bags)
Example: deceased
To cope with these, analysts can handpick disruptive attributes
These are left out when searching
4/14/2019
T.K. Cocx,

12. Semantic Split
The criminal record database has two parts that are clearly semantically different
Demographic data (also known for non criminals)
Crime data
They are strictly separated number-wise.
Analyst can handpick an x that states the beginning of the second halve.
1:N / N:1
Lower and upper halve
Pick 1 item maximum from the lower halve.
For example: born / ethniticity.
4/14/2019
T.K. Cocx,

13. Subset Search
Support: number of occurrences (set to true) of an attribute.
When support reaches threshold it is considered common.
Not commonness one wants but interestingness
Female
Confidence: conditional probability of a certain itemset given another itemset.
If a certain itemset that is ‘interesting’ implies another: the combination is also interesting
4/14/2019
T.K. Cocx,

14. Subset Search (Cont.) Confidence can be seen as:
max(C(a,b),C(b,a))
avg(C(a,b),C(b,a))
The latter is stronger because it demands an implication in both directions.
An itemset will certainly be interesting if its occurence is much higher than one would expect based upon the occurence of its individual member-itemsets:
Lift
The relation between expected occurence and actual occurrence.
4/14/2019
T.K. Cocx,

15. Visualization
All interesting item sets are put in a giant tree that is represented to the user
In this tree each node is part of an interesting subset of the database with all its parents.
NOT siblings 10 5
Interesting sets:
10 – 1
10 – 1 – 2
10 – 4
5 – 7 1 4 7 2
4/14/2019
T.K. Cocx,

16. Visualization (Cont.)
It is common practice to represent the tree with all its member subsets
Inpractical, especially for police analyst
Use known paradigm
Resulting dataset
4/14/2019
T.K. Cocx,

17. Some notable results
Joyriding ↔ Violation of Work Circumstances ↔ Alcohol Addiction
Drug Smuggling ↔ Drug Addiction
Manslaughter ↔ Discrimination
Male ↔ Theft with Violence ↔ Possession (of weapon)
Female ↔ Drug Abuse
African Descend↔ Public Safety
Rural Areas ↔ Traffic Felonies
4/14/2019
T.K. Cocx,

18. Conclusion & Future work
The nature of the criminal record database needs a custom set of specific solutions
Attribute ban, semantic split and visualization contribute largely to results from performed queries.
Semantic bond between single attributes
Search for most uncommon itemsets
Inherently uncommon couples. (semantic bond)
Comparison with social sciences
4/14/2019
T.K. Cocx,

19. Interrogation
4/14/2019
T.K. Cocx,