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Identifying glass

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?

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Identifying glass

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  1. 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.

  2. 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?

  3. Research data: Attributes • Refractive Index (1.517865) • Sodium (6.62%) • Magnesium (3.73%) • Aluminum (0.45%) • Silicon (76.38%) • Potassium (8.65%) • Calcium (2.76%) • Barium (0.40%) • Iron (1.01%)

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

  5. 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

  6. Headlamps Run down?! Hit ‘n Run?

  7. Possible suspects • Jan-Peter- Has no drivers license- No previous criminal records • Bea- No longer valid to drive because of age- Is always escorted by motors, can not run down persons.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!

  8. Using our data • Jan-Peter- Has no drivers license- No previous criminal records • Bea- No longer valid to drive because of age- Is always escorted by motors, can not run down persons.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!

  9. 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!

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