Data Preprocessing in Data mining, Lecture notes of Data Mining

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Data

Preprocessing

Techniques

Chapter 3: Data Preprocessing



Data Preprocessing: An Overview



Data Quality



Major Tasks in Data Preprocessing



Data Cleaning



Data Integration



Data Reduction



Data Transformation and Data Discretization



Summary

Major Tasks in Data Preprocessing

 Data cleaning



Fill in missing values, smooth noisy data, identify or

remove outliers, and resolve inconsistencies



Data integration

 Integration of multiple databases, data cubes, or files

 Data reduction



Dimensionality reduction

 Numerosity reduction



Data compression

 Data transformation and data discretization

 Normalization



Concept hierarchy generation

Chapter 3: Data Preprocessing



Data Preprocessing: An Overview



Data Quality



Major Tasks in Data Preprocessing



Data Cleaning



Data Integration



Data Reduction



Data Transformation and Data Discretization



Summary

Incomplete (Missing) Data  Data is not always available  E.g., many tuples have no recorded value for several attributes, such as customer income in sales data  (^) Missing data may be due to  equipment malfunction  inconsistent with other recorded data and thus deleted  data not entered due to misunderstanding  certain data may not be considered important at the time of entry  not register history or changes of the data  (^) Missing data may need to be inferred

How to Handle Missing Data?  Ignore the tuple: usually done when class label is missing (when doing classification)—not effective when the % of missing values per attribute varies considerably  Fill in the missing value manually: tedious + infeasible?  Fill in it automatically with  a global constant : e.g., “unknown”, a new class?!  the attribute mean  the attribute mean for all samples belonging to the same class: smarter  the most probable value: inference-based such as Bayesian formula or decision tree

How to Handle Noisy Data?

 Binning



first sort data and partition into (equal-frequency)

bins



then one can smooth by bin means, smooth by bin

median, smooth by bin boundaries, etc.



Regression



smooth by fitting the data into regression functions



Clustering



detect and remove outliers



Combined computer and human inspection



detect suspicious values and check by human

(e.g., deal with possible outliers)

Data Cleaning as a Process  (^) Data discrepancy detection  (^) Use metadata (e.g., domain, range, dependency, distribution)  (^) Check field overloading  (^) Check uniqueness rule, consecutive rule and null rule  (^) Use commercial tools  (^) Data scrubbing: use simple domain knowledge (e.g., postal code, spell-check) to detect errors and make corrections  (^) Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers)  (^) Data migration and integration  (^) Data migration tools: allow transformations to be specified  (^) ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface  (^) Integration of the two processes  (^) Iterative and interactive (e.g., Potter’s Wheels)

Data Integration  (^) Data integration :  (^) Combines data from multiple sources into a coherent store  (^) Schema integration: e.g., A.cust-id  B.cust-#  (^) Integrate metadata from different sources  (^) Entity identification problem:  (^) Identify real world entities from multiple data sources, e.g., Bill Clinton = William Clinton  (^) Detecting and resolving data value conflicts  For the same real world entity, attribute values from different sources are different  (^) Possible reasons: different representations, different scales, e.g., metric vs. British units

Handling Redundancy in Data

Integration

 Redundant data occur often when integration of multiple databases  Object identification : The same attribute or object may have different names in different databases  Derivable data: One attribute may be a “derived” attribute in another table, e.g., annual revenue  (^) Redundant attributes may be able to be detected by correlation analysis and covariance analysis  Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality

Chi-Square Calculation: An

Example



Χ^2 (chi-square) calculation (numbers in parenthesis

are expected counts calculated based on the data

distribution in the two categories)



It shows that like_science_fiction and play_chess

are correlated in the group

507. 93 840 ( 1000 840 ) 360 ( 200 360 ) 210 ( 50 210 ) 90 ( 250 90 ) 2 2 2 2 2           Play chess Not play chess Sum (row) Like science fiction 250(90) 200(360) 450 Not like science fiction 50(210) 1000(840) 1050 Sum(col.) 300 1200 1500

Correlation Analysis (Numeric Data)



Correlation coefficient (also called Pearson’s product

moment coefficient)

where n is the number of tuples, and are the respective means of A and B, σA and σB are the respective standard deviation of A and B, and Σ(aibi) is the sum of the AB cross- product.

 If r

A,B > 0, A and B are positively correlated (A’s values

increase as B’s). The higher, the stronger correlation.

 r

A,B = 0: independent;^ rAB < 0: negatively correlated

A B n i i i A B n i i i A B n ab nA B n a A b B r   ( 1 )  ( ) ( 1 ) ( )( ) 1 1 ,       

A B

Co-Variance: An Example  (^) It can be simplified in computation as  (^) Suppose two stocks A and B have the following values in one week: (2, 5), (3, 8), (5, 10), (4, 11), (6, 14).  (^) Question: If the stocks are affected by the same industry trends, will their prices rise or fall together?  (^) E(A) = (2 + 3 + 5 + 4 + 6)/ 5 = 20/5 = 4  E(B) = (5 + 8 + 10 + 11 + 14) /5 = 48/5 = 9.  (^) Cov(A,B) = (2×5+3×8+5×10+4×11+6×14)/5 − 4 × 9.6 = 4  (^) Thus, A and B rise together since Cov(A, B) > 0.

Chapter 3: Data Preprocessing



Data Preprocessing: An Overview



Data Quality



Major Tasks in Data Preprocessing



Data Cleaning



Data Integration



Data Reduction



Data Transformation and Data Discretization



Summary