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Data Mining:Classification

Learn about the classification problem, techniques, algorithms, and examples in data mining. Understand regression, decision trees, neural networks, and more for predictive modeling.

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Data Mining:Classification

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  1. Data Mining:Classification Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari

  2. Classification Outline Goal:Provide an overview of the classification problem and introduce some of the basic algorithms • Classification Problem Overview • Classification Techniques • Regression • Distance • Decision Trees • Rules • Neural Networks

  3. Classification Problem • Given a database D={t1,t2,…,tn} and a set of classes C={C1,…,Cm}, the Classification Problem is to define a mapping f:DgC where each ti is assigned to one class. • Actually divides D into equivalence classes. • Predictionis similar, but may be viewed as having infinite number of classes.

  4. Classification Examples • Teachers classify students’ grades as A, B, C, D, or F. • Identify mushrooms as poisonous or edible. • Predict when a river will flood. • Identify individuals with credit risks. • Speech recognition • Pattern recognition

  5. x <90 >=90 <70 <50 >=60 >=70 Classification Ex: Grading • If x >= 90 then grade =A. • If 80<=x<90 then grade =B. • If 70<=x<80 then grade =C. • If 60<=x<70 then grade =D. • If x<50 then grade =F. x A <80 >=80 x B x C D F

  6. Classification Ex: Letter Recognition View letters as constructed from 5 components: Letter A Letter B Letter C Letter D Letter E Letter F

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