chap5_alternat_classif_sh

chap5_alternat_classif_sh - Data Mining Classification:...

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Unformatted text preview: Data Mining Classification: Alternative Techniques Lecture Notes for Chapter 5 Introduction to Data Mining by Tan, Steinbach, Kumar Edited for STATS202, Stanford University, Fall 2010 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 Rule-Based Classifier z Classify records by using a collection of ifthen rules z Rule: ( Condition ) y where Condition is a conjunctions of attributes y is the class label LHS : rule antecedent or condition RHS : rule consequent Examples of classification rules: (Blood Type=Warm) (Lay Eggs=Yes) Birds (Taxable Income < 50K) (Refund=Yes) Evade=No Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3 Rule-based Classifier (Example) R1: (Give Birth = no) (Can Fly = yes) Birds R2: (Give Birth = no) (Live in Water = yes) Fishes R3: (Give Birth = yes) (Blood Type = warm) Mammals R4: (Give Birth = no) (Can Fly = no) Reptiles R5: (Live in Water = sometimes) Amphibians Name Blood Type Give Birth Can Fly Live in Water Class human warm yes no no mammals python cold no no no reptiles salmon cold no no yes fishes whale warm yes no yes mammals frog cold no no sometimes amphibians komodo cold no no no reptiles bat warm yes yes no mammals pigeon warm no yes no birds cat warm yes no no mammals leopard shark cold yes no yes fishes turtle cold no no sometimes reptiles penguin warm no no sometimes birds porcupine warm yes no no mammals eel cold no no yes fishes salamander cold no no sometimes amphibians gila monster cold no no no reptiles platypus warm no no no mammals owl warm no yes no birds dolphin warm yes no yes mammals eagle warm no yes no birds Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4 Application of Rule-Based Classifier z A rule r covers an instance x if the attributes of the instance satisfy the condition of the rule R1: (Give Birth = no) (Can Fly = yes) Birds R2: (Give Birth = no) (Live in Water = yes) Fishes R3: (Give Birth = yes) (Blood Type = warm) Mammals R4: (Give Birth = no) (Can Fly = no) Reptiles R5: (Live in Water = sometimes) Amphibians The rule R1 covers a hawk => Bird The rule R3 covers the grizzly bear => Mammal Name Blood Type Give Birth Can Fly Live in Water Class hawk warm no yes no ? grizzly bear warm yes no no ? Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 5 Rule Coverage and Accuracy z Coverage of a rule: Fraction of records that satisfy the antecedent of a rule z Accuracy of a rule: Fraction of records that satisfy both the antecedent and consequent of a rule Tid Refund Marital Status Taxable Income Class 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes (Status=Single) No Coverage = 40%, Accuracy = 50% Tan,Steinbach, Kumar...
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This note was uploaded on 07/29/2011 for the course STAT 202 at Stanford.

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chap5_alternat_classif_sh - Data Mining Classification:...

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