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UNIT-2 Data Preprocessing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Lecture-13 Why preprocess the data?
Lecture-13 Why Data Preprocessing? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-13 Why Data Preprocessing?
Multi-Dimensional Measure of Data Quality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-13 Why Data Preprocessing?
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],Lecture-13 Why Data Preprocessing?
Forms of data preprocessing Lecture-13 Why Data Preprocessing?
[object Object],[object Object]
Data Cleaning ,[object Object],[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
Missing Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
How to Handle Missing Data? ,[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
How to Handle Missing Data? ,[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
Noisy Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
How to Handle Noisy Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
Simple Discretization Methods: Binning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-14 - Data cleaning
Binning Methods for Data Smoothing ,[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],Lecture-14 - Data cleaning
Cluster Analysis Lecture-14 - Data cleaning
Regression x y y = x + 1 X1 Y1 Y1’ Lecture-14 - Data cleaning
[object Object],[object Object]
Data Integration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-15 - Data integration and transformation
Handling Redundant Data in Data Integration ,[object Object],[object Object],[object Object],Lecture-15 - Data integration and transformation
Handling Redundant Data in Data Integration ,[object Object],[object Object],Lecture-15 - Data integration and transformation
Data Transformation ,[object Object],[object Object],[object Object],Lecture-15 - Data integration and transformation
Data Transformation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-15 - Data integration and transformation
Data Transformation: Normalization ,[object Object],[object Object],[object Object],Where j is the smallest integer such that Max(| |)<1 Lecture-15 - Data integration and transformation
[object Object],[object Object]
Data Reduction ,[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Data Reduction Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Data Cube Aggregation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Dimensionality Reduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Wavelet Transforms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction Haar2 Daubechie4
[object Object],[object Object],[object Object],[object Object],[object Object],Principal Component Analysis Lecture-16 - Data reduction
X1 X2 Y1 Y2 Principal Component Analysis Lecture-16 - Data reduction
Attribute subset selection ,[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Heuristic Selection Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Example of Decision Tree Induction Initial attribute set: {A1, A2, A3, A4, A5, A6} A4 ? A1? A6? Class 1 Class 2 Class 1 Class 2 Reduced attribute set: {A1, A4, A6} Lecture-16 - Data reduction >
Numerosity Reduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Regression and Log-Linear Models ,[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Regress Analysis and Log-Linear Models Lecture-16 - Data reduction
Histograms ,[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Clustering ,[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Sampling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-16 - Data reduction
Sampling SRSWOR (simple random sample without replacement) SRSWR Lecture-16 - Data reduction Raw Data
Sampling Raw Data Cluster/Stratified Sample Lecture-16 - Data reduction
[object Object],[object Object]
Discretization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-17 - Discretization and concept hierarchy generation
Discretization and Concept hierachy ,[object Object],[object Object],[object Object],[object Object],Lecture-17 - Discretization and concept hierarchy generation
Discretization and concept hierarchy generation for numeric data ,[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-17 - Discretization and concept hierarchy generation
Entropy-Based Discretization ,[object Object],[object Object],[object Object],[object Object],Lecture-17 - Discretization and concept hierarchy generation
Discretization by intuitive partitioning ,[object Object],[object Object],[object Object],[object Object],[object Object],Lecture-17 - Discretization and concept hierarchy generation
Example of 3-4-5 rule (-$4000 -$5,000) Step 4: Lecture-17 - Discretization and concept hierarchy generation (-$400 - 0) (-$400 - -$300) (-$300 - -$200) (-$200 - -$100) (-$100 - 0) (0 - $1,000) (0 - $200) ($200 - $400) ($400 - $600) ($600 - $800) ($800 - $1,000) ($2,000 - $5, 000) ($2,000 - $3,000) ($3,000 - $4,000) ($4,000 - $5,000) ($1,000 - $2, 000) ($1,000 - $1,200) ($1,200 - $1,400) ($1,400 - $1,600) ($1,600 - $1,800) ($1,800 - $2,000) msd=1,000 Low=-$1,000 High=$2,000 Step 2: Step 1: -$351 -$159 profit $1,838 $4,700 Min Low (i.e, 5%-tile) High(i.e, 95%-0 tile) Max count (-$1,000 - $2,000) (-$1,000 - 0) (0 -$ 1,000) Step 3: ($1,000 - $2,000)
Concept hierarchy generation for categorical data ,[object Object],[object Object],[object Object],[object Object],Lecture-17 - Discretization and concept hierarchy generation
Specification of a set of attributes ,[object Object],country province_or_ state city street 15 distinct values 65 distinct values 3567 distinct values 674,339 distinct values Lecture-17 - Discretization and concept hierarchy generation

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data warehousing & minining 1st unit

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  • 6. Forms of data preprocessing Lecture-13 Why Data Preprocessing?
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  • 16. Cluster Analysis Lecture-14 - Data cleaning
  • 17. Regression x y y = x + 1 X1 Y1 Y1’ Lecture-14 - Data cleaning
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  • 32. X1 X2 Y1 Y2 Principal Component Analysis Lecture-16 - Data reduction
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  • 35. Example of Decision Tree Induction Initial attribute set: {A1, A2, A3, A4, A5, A6} A4 ? A1? A6? Class 1 Class 2 Class 1 Class 2 Reduced attribute set: {A1, A4, A6} Lecture-16 - Data reduction >
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  • 42. Sampling SRSWOR (simple random sample without replacement) SRSWR Lecture-16 - Data reduction Raw Data
  • 43. Sampling Raw Data Cluster/Stratified Sample Lecture-16 - Data reduction
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  • 50. Example of 3-4-5 rule (-$4000 -$5,000) Step 4: Lecture-17 - Discretization and concept hierarchy generation (-$400 - 0) (-$400 - -$300) (-$300 - -$200) (-$200 - -$100) (-$100 - 0) (0 - $1,000) (0 - $200) ($200 - $400) ($400 - $600) ($600 - $800) ($800 - $1,000) ($2,000 - $5, 000) ($2,000 - $3,000) ($3,000 - $4,000) ($4,000 - $5,000) ($1,000 - $2, 000) ($1,000 - $1,200) ($1,200 - $1,400) ($1,400 - $1,600) ($1,600 - $1,800) ($1,800 - $2,000) msd=1,000 Low=-$1,000 High=$2,000 Step 2: Step 1: -$351 -$159 profit $1,838 $4,700 Min Low (i.e, 5%-tile) High(i.e, 95%-0 tile) Max count (-$1,000 - $2,000) (-$1,000 - 0) (0 -$ 1,000) Step 3: ($1,000 - $2,000)
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