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Data Preprocessing

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Object-Frontier Software Pvt. Ltd
Object-Frontier Software Pvt. Ltd

a presentation on data preprocessing concepts

Tecnologia
1 de 41
Introduction to Data Mining Ch. 2 Data Preprocessing Heon Gyu Lee ( [email_address] ) http://dblab.chungbuk.ac.kr/~hglee DB/Bioinfo., Lab. http://dblab.chungbuk.ac.kr Chungbuk National University
Why Data Preprocessing? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What is Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Attributes Objects
Types of Attributes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discrete and Continuous Attributes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Quality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Noise ,[object Object],[object Object],Two Sine Waves Two Sine Waves + Noise
Outliers ,[object Object]
Missing Values ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Duplicate Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Major Tasks in Data Preprocessing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Forms of Data Preprocessing
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data Cleaning
Data Cleaning : How to Handle Missing Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Cleaning : How to Handle Noisy Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Cleaning : Binning Methods ,[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]
Data Cleaning : Regression x y y = x + 1 X1 Y1 Y1’
Data Cleaning : Cluster Analysis
Data Integration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Integration : Handling Redundancy in Data Integration ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Integration : Correlation Analysis (Numerical Data) ,[object Object],[object Object],[object Object],[object Object]
Data Integration : Correlation Analysis (Categorical Data) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chi-Square Calculation: An Example ,[object Object],[object Object],1500 1200 300 Sum(col.) 1050 1000(840) 50(210) Not like science fiction 450 200(360) 250(90) Like science fiction Sum (row) Not play chess Play chess
Data Transformation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Transformation : Normalization ,[object Object],[object Object],[object Object],[object Object],[object Object],Where j is the smallest integer such that Max(| ν ’ |) < 1
Data Reduction Strategies ,[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]
Data Reduction : Aggregation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Reduction : Aggregation Standard Deviation of Average Monthly Precipitation Standard Deviation of Average Yearly Precipitation Variation of Precipitation in Australia
Data Reduction : Sampling ,[object Object],[object Object],[object Object],[object Object]
Data Reduction : Types of Sampling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Reduction : Dimensionality Reduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dimensionality Reduction : PCA ,[object Object],x 2 x 1 e
Dimensionality Reduction : PCA ,[object Object],[object Object],x 2 x 1 e
Data Reduction : Feature Subset Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Reduction : Feature Subset Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Reduction : Feature Creation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Reduction : Mapping Data to a New Space Two Sine Waves Two Sine Waves + Noise Frequency ,[object Object],[object Object]
Data Reduction : Discretization Using Class Labels ,[object Object],3 categories for both x and y 5 categories for both x and y
Data Reduction : Discretization Without Using Class Labels Data Equal interval width Equal frequency K-means
Data Reduction : Attribute Transformation ,[object Object],[object Object],[object Object]
Question & Answer

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Data Preprocessing

  • 1. Introduction to Data Mining Ch. 2 Data Preprocessing Heon Gyu Lee ( [email_address] ) http://dblab.chungbuk.ac.kr/~hglee DB/Bioinfo., Lab. http://dblab.chungbuk.ac.kr Chungbuk National University
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. Forms of Data Preprocessing
  • 13.
  • 14.
  • 15.
  • 16.
  • 17. Data Cleaning : Regression x y y = x + 1 X1 Y1 Y1’
  • 18. Data Cleaning : Cluster Analysis
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28. Data Reduction : Aggregation Standard Deviation of Average Monthly Precipitation Standard Deviation of Average Yearly Precipitation Variation of Precipitation in Australia
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39. Data Reduction : Discretization Without Using Class Labels Data Equal interval width Equal frequency K-means
  • 40.