Identifying glass Using the top-notch data-mining algorithms from the Leiden Institute of Advanced Computer Science (LIACS) Presented by Jan-Willem and.

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Presentation transcript:

Identifying glass Using the top-notch data-mining algorithms from the Leiden Institute of Advanced Computer Science (LIACS) Presented by Jan-Willem and Frans-Willem.

Why identifying glass? Crime scene: glass found in leg of murdered person. Where did the glass come from? Might this have been the cause of death?

Research data: Attributes Refractive Index ( ) Sodium (6.62%) Magnesium (3.73%) Aluminum (0.45%) Silicon (76.38%) Potassium (8.65%) Calcium (2.76%) Barium (0.40%) Iron (1.01%)

Decision Tree Barium <= 0.27% –(not relevant) Barium > 0.27% –Silicon <= 70.16%: build wind non-float –Silicon > 70.16%: headlamps

Filling in our data Data: Barium 0.40%, Silicon 76.38% Barium <= 0.27% –(not relevant) Barium > 0.27% –Silicon <= 70.16%: build wind non-float –Silicon > 70.16%: headlamps

Headlamps Run down?! Hit ‘n Run?

Possible suspects Jan-Peter - Has no drivers license - No previous criminal records Bea - No longer valid to drive because of age - Is always escorted by motors, can not run down persons. Peter - Has just got his drivers license - Left-headlight duct taped!

Using our data Jan-Peter - Has no drivers license - No previous criminal records Bea - No longer valid to drive because of age - Is always escorted by motors, can not run down persons. Peter - Has just got his drivers license - Left-headlight duct taped!

Conclusion After investigating the car of Peter, and taking samples of his headlights, we found out that it matched the exact percentages as the glass particle found in the victims leg. This was enough to convince the judge, and get him behind bars for his lifetime!