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Vehicle

Vehicle . We had to design a data mining tool, to identify 4 different types of vehicles based on their silhouettes. This could be used for a toll gate, which charges different prices for different types of vehicles. Frank and Nick. The problem and the goal.

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Vehicle

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  1. Vehicle • We had to design a data mining tool, to identify 4 different types of vehicles based on their silhouettes. • This could be used for a toll gate, which charges different prices for different types of vehicles. Frank and Nick

  2. The problem and the goal To classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles.

  3. Description of data • There are 19 attributes in the data set • There are 846 instances in the data set

  4. Description of data • Some of the attributes include: • Compactness • Circularity • Scatter ratio • Scaled radius of gyration • Radius ratio • Etc.

  5. Classification accuracy • Decision stump: 38.5% • Decision tree: 69.7% • ZeroR: 21.5%

  6. Interesting patterns • If max length aspect ratio is between 7.3 and infinity then it’s a bus • If pr. Axis rectangularity is between 25.4 and infinity then it’s a bus

  7. Association rules • If pr. axis rectangularity is between 18.2 and 19.4 then scatter ratio is between 142.6 and 157.9 • If elongatedness is between 43.5 and 47 and pr. axis rectangularity is between 18.2 and 19.4 then scatter ratio is between 142.6 and 157.9

  8. Practical use • The results could be used to determine the price that a vehicle has to pay at a toll gate, depending on what type of vehicle it is.

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