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Data Mining CSCI  307, Spring Data Mining CSCI  307, Spring

Data Mining CSCI 307, Spring - PowerPoint Presentation

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Data Mining CSCI 307, Spring - PPT Presentation

2019 Lecture 17 Covering algorithms II 1 Covering Example continued 2 Age Spectacle prescription Astigmatism Tear production rate Recommended lenses Young Myope No Reduced None Young ID: 920862

prescription spectacle age rate spectacle prescription rate age production astigmatism tear rule normal rules presbyopic instances myope prod part

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Slide1

Data MiningCSCI 307, Spring 2019Lecture 17

Covering algorithms II

1

Slide2

Covering Example continued

2

Age

Spectacle prescription

Astigmatism

Tear production rateRecommended lensesYoungMyopeNoReducedNoneYoungMyopeNoNormalSoftYoungMyopeYesReducedNoneYoungHypermetropeNoReducedNoneYoungHypermetropeNoNormalSoftYoungHypermetropeYesReducedNoneYoungHypermetropeYesNormalHardPre-presbyopicMyopeNoReducedNonePre-presbyopicMyopeNoNormalSoftPre-presbyopicMyopeYesReducedNonePre-presbyopicHypermetropeNoReducedNonePre-presbyopicHypermetropeNoNormalSoftPre-presbyopicHypermetropeYesReducedNonePre-presbyopicHypermetropeYesNormalNonePresbyopicMyopeNoReducedNonePresbyopicMyopeNoNormalNonePresbyopicMyopeYesReducedNonePresbyopicHypermetropeNoReducedNonePresbyopicHypermetropeNoNormalSoftPresbyopicHypermetropeYesReducedNonePresbyopicHypermetropeYesNormalNone

After first rule established, delete instances covered by the first rule and start again.

"Fresh" data set

Slide3

Example part 2: Contact Lens DataRule we seek:Possible Tests:

3

Age = Young

Age = Pre-

presbyopic

Age = Presbyopic Spectacle prescription = Myope Spectacle prescription = HypermetropeAstigmatism = noAstigmatism = yesTear production rate = ReducedTear production rate = Normal

Slide4

part 2: Modified Rule and Resulting DataRule with the best test is added:Instances covered by modified rule:

4

Age

Spectacle prescription

Astigmatism

Tear production rateRecommended lensesYoungMyopeNoReducedNoneYoungMyopeNoNormalSoftYoungMyopeYesReducedNoneYoungHypermetropeNoReducedNoneYoungHypermetropeNoNormalSoftYoungHypermetropeYesReducedNoneYoungHypermetropeYesNormalHard

Slide5

Example part 2: RefineRule we seek:Possible Tests:

5

Spectacle prescription =

Myope

Spectacle prescription = HypermetropeAstigmatism = noAstigmatism = yesTear production rate = ReducedTear production rate = Normal

Slide6

part 2: Modified Rule and Resulting DataRule with the best test is added:Instances covered by modified rule:

6

Age

Spectacle prescription

Astigmatism

Tear production rateRecommended lensesYoungMyopeYesReducedNoneYoungHypermetropeYesReducedNoneYoungHypermetropeYesNormalHard

Slide7

Example part 2: Refine MoreRule we seek:Possible Tests:

7

Spectacle prescription =

Myope

Spectacle prescription = HypermetropeTear production rate = ReducedTear production rate = Normal

Slide8

The Result

8

Final Rules:

Second rule for recommending "hard lenses":

(build from the instances not covered by the first rule.)

If astigmatism = yes and tear production rate = normal and spectacle prescription = myope then recommendation = hard

Slide9

The Rest of the PRISM Rules

9

If

astigmatism = no

and

tear-prod-rate = normal and spectacle-prescription = hypermetrope then softIf astigmatism = no and tear-prod-rate = normal and age = young then softIf age = pre-presbyopic and astigmatism = no and tear-prod-rate = normal then softIf tear-prod-rate = reduced then noneIf age = presbyopic and tear-prod-rate = normal and spectacle-prescription = myope and astigmatism = no then noneIf spectacle-prescrip = hypermetrope and astigmatism = yes and age = pre-presbyopic then noneIf age = presbyopic and spectacle-prescription = hypermetrope and astigmatism = yes then none

Slide10

Pseudo-Code for PRISM

10

Slide11

Rules versus Decision ListsPRISM with outer loop removed generates a decision list for one classSubsequent rules are designed for rules that are not covered by previous rulesBut: order doesn’t matter because all rules predict the same classOuter loop considers all classes separatelyNo order dependence impliedProblems: overlapping rules, default rule required

11

Slide12

Separate and ConquerMethods like PRISM (for dealing with one class) are separate-and-conquer algorithms:First, identify a useful ruleThen, separate out all the instances it coversFinally, “conquer” the remaining instances

12