1. A Method for the Comparison of Criminal Cases using digital documents
A New Distance Measure
T.K. Cocx,
5/23/2019

2. 5/23/2019
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3. 5/23/2019
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4. Comparing Documents
Data mining: the search for knowledge in large amounts of data.
Data: digital documents found on crime scene or fabricated by police describing the crime scene
Knowledge: what crime labs may be setup by the same group of criminals
Data mining tools:
Text mining: extraction of entities from documents
Distance measure on extracted output: document similarity
Visualization: clustering of documents on screen
5/23/2019
T.K. Cocx,

5. Coupled investigation table
4-step paradigm
Documents
Extraction table
Investigation
Amount
Type
Entity
Text mining
Coupled investigation table
In Common
Investigation 2
Investigation 1
Transformation
Distance Matrix
0.92
0.27 …
0.51 2 1
Distance Measure
Clustering
Visualization
5/23/2019
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6. Process characteristics
Documents
Contain potentially useful information, but is
Usually unstructured
Typing mistakes
Police reports: polluted with terminology
Text mining
the process of extracting interesting and non-trivial information and knowledge from unstructured text.
Names, locations, cars (plates), products, url’s address, IP’s
Language bound
5/23/2019
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7. Process characteristics
Extraction table
Primary key: entity & investigation so,
The table stores all occurrences of entities and their respective types in the investigations
Wim, person, inv liacs
Joost, person, inv liacs
Sjoerd, person, inv liacs
Sjoerd, person, inv mi
Joost, person, inv lumc
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8. Process characteristics
Transformation
Goal: compare investigations
Transform table to an investigation primary keyed table.
All investigations contain Boolean information about occurrence of particular entity.
Number of field  number of different entities.
Could be clustered in ‘n’ dimensions.
Should be downscaled
Coupled investigation table
Contains less dimensional data about investigations
5/23/2019
T.K. Cocx,

9. Process characteristics
Distance measure
Use overlap in occurrences to constitute distance
More overlap  closer
The closer two investigations are, the more similar they are, the more likely they are related to the same group of criminals.
Between 0 and 1
Difference in size
Supermarket vs. police investigation
Relative comparison
Distance Matrix
Contains all distances
5/23/2019
T.K. Cocx,

10. Process characteristics
Visualization
Distance not necessarily defined in 2 dimensions
In some way display investigations as correctly as possible
Employs iterative push and pull technique
Clustering
Investigation comparison report easily readable by police analyst.
5/23/2019
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11. On to the details… “So, how does this all actually works??” 5/23/2019
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12. Text mining No large-scale domain specific text miner available.
Police decision dictates employment of SPSS LexiQuest
No filtering on police terminology
Based upon English engine
Wrong classification of entities in approximately 78% of time (incl. 68%% classified as Unknown)
5/23/2019
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13. Table transformation
Use simple SQL to transform the extraction table to a high dimensional table.
This table contains the occurrences per investigation:
5/23/2019
T.K. Cocx,

14. Distance Measure: supermarket
Comparison of shopping behavior
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15. Comparison shopping & crime
Crime: no information does not constitute dissimilarity
Incorporate size while taking this into account
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16. New distance measure
Use difference between statistical expected amount of common entities and actual overlapping amount.
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17. Problem: A What is the total Universe of entities Language: infeasible
Total amount of distinct entities in database
Invert expected value function to calculate average universe size:
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18. Employ normal distribution
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19. Distance function
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20. Resulting graphs
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21. Visualization
Impossible to display clustering in 2 dimensions perfectly
Approach best possible fit
Place all investigations randomly in the X,Y plane
Calculate couple wise error made in placement.
Correct couple wise through push and pull technique
Repeat from 2 until total error is at a (local) minimum
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22. Push and Pull
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23. Visualization Example
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24. Results Universe A Universe Averaged 5/23/2019
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25. Future research
Domain specific/ domain trained text miner necessary to improve results.
Qualitative police feedback on results
Incorporating this feedback in design decisions
Use number of occurrences instead of Booleans
Select on type (omit Unknown)
Choose between different universes
5/23/2019
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26. Demonstration
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27. Interrogation
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