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Data Mining Concepts

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Ho Viet Lam - Nguyen Thi My Dung May, 14th 2007

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Data Mining Concept Ho Viet Lam - Nguyen Thi My Dung May, 14 th 2007
Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object]
What is data mining? ,[object Object],[object Object]
Why data mining? ,[object Object],[object Object],[object Object],[object Object]
On what kind of data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Structure - 3D Anatomy Function – 1D Signal Metadata – Annotation
Overview of data mining technology ,[object Object],[object Object],[object Object],[object Object]
Data Mining vs. Data Warehousing ,[object Object],[object Object]
Knowledge Discovery in Databases and Data Mining ,[object Object],[object Object]
Goals of Data Mining and KDD ,[object Object],[object Object],[object Object],[object Object]
Types of Knowledge Discovery during Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Content
Association Rules ,[object Object],[object Object],[object Object]
Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object]
Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bread, cookies, coffee 8:40 1735 Milk, eggs 8:05 1130 Milk, juice 7:38 792 Bread, Milk, cookies, juice 6:35 101 Items-Bought time Transaction-id 1 Coffee 1 Eggs 2 Juice 2 Cookies 2 Bread 3 Milk support Item
Association Rules ,[object Object],[object Object],[object Object]
Association Rules ,[object Object],D mins = 2 minf = 0.5 freq > 0.5 Bread, cookies, coffee 8:40 1735 Milk, eggs 8:05 1130 Milk, juice 7:38 792 Bread, milk, cookies, juice 6:35 101 Items-Bought time Transaction-id 1 Eggs 1 Coffee 2 Juice 2 Cookies 2 Bread 3 Milk support Item 0.75, 0.5, 0.5, 0.5, 0.25, 0.25 milk, bread, juice, cookies, eggs, coffee The candidate 1-itemsets 0.75, 0.5, 0.5, 0.5 milk, bread, juice, cookies frequent 1-itemsets 0.25, 0.5, 0.25, 0.25, 0.5, 0.25 {milk, bread}, {milk, juice}, {bread, juice}, {milk, cookies}, {bread, cookies}, {juice, cookies} The candidate 2-itemsets 0.5, 0.5 {milk, juice}, {bread, cookies} frequent 2-itemsets {……………….} {……………..} The candidate 3-itemsets Ф Ф frequent 3-itemsets milk, bread, juice, cookies, {milk, juice}, {bread, cookies} result
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Association Rules ,[object Object]
Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object]
Association Rules ,[object Object],[object Object],[object Object]
Association Rules ,[object Object],Item head table Root Bread, milk, cookies, juice Milk, bread, cookies, juice Milk:1 Bread:1 Cookies:1 Juice:1 Milk, juice Juice:1 Milk:2 Milk, eggs Milk Milk: 3 Bread, cookies, coffee Bread, cookies Bread:1 Cookies:1 Transaction 1 Transaction 2 Transaction 3 Transaction 4 2 juice 2 cookies 2 bread 3 Milk link Support Item
Association Rules ,[object Object],Root Bread:1 Cookies:1 Juice:1 Juice:1 Milk: 3 Bread:1 Cookies:1 Milk, juice Bread, cookies Milk bread cookies juice
Association Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Association Rules ,[object Object]
Classification ,[object Object],[object Object],[object Object],married salary Acct balance age Yes <20k Poor risk >=20k <50k Fair risk >=50 Good risk no <5k Poor risk >=25 <25 >5k Fair risk Good risk
Class attribute Expected information Salary I(3,3)=1 Information gain Gain(A) = I-E(A) E(Married)=0.92 Gain(Married)=0.08 E(Salary)=0.33 Gain(Salary)= 0.67 E(A.balance)=0.82 Gain(A.balance)=0.18 E(Age)=0.81 Gain(Age)= 0.19 age Class is “no” {4,5} >=50k 20k..50k <20k Class is “no” {3} Class is “yes” {6} Class is “yes” {1,2} Entropy <25 >=25 Yes >=25 >=5k 20k..50k Yes 6 No >=25 <5k <20k No 5 No <25 >=5k <20k No 4 No <25 <5k 20k..50k Yes 3 Yes >=25 >=5k >=50 Yes 2 Yes >=25 <5k >=50 No 1 Loanworthy Age Acct balance Salary Married RID
Classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification ,[object Object]
Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering ,[object Object],[object Object],[object Object],[object Object]
Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering ,[object Object]
Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications of data mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications of data mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Commercial tools ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],Thanks for listening!!!

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Data Mining Concepts

  • 1. Data Mining Concept Ho Viet Lam - Nguyen Thi My Dung May, 14 th 2007
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  • 27. Class attribute Expected information Salary I(3,3)=1 Information gain Gain(A) = I-E(A) E(Married)=0.92 Gain(Married)=0.08 E(Salary)=0.33 Gain(Salary)= 0.67 E(A.balance)=0.82 Gain(A.balance)=0.18 E(Age)=0.81 Gain(Age)= 0.19 age Class is “no” {4,5} >=50k 20k..50k <20k Class is “no” {3} Class is “yes” {6} Class is “yes” {1,2} Entropy <25 >=25 Yes >=25 >=5k 20k..50k Yes 6 No >=25 <5k <20k No 5 No <25 >=5k <20k No 4 No <25 <5k 20k..50k Yes 3 Yes >=25 >=5k >=50 Yes 2 Yes >=25 <5k >=50 No 1 Loanworthy Age Acct balance Salary Married RID
  • 28.
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