北京航空航天大学：《数据挖掘——概念和技术（Data Mining - Concepts and Techniques）》课程教学资源（PPT课件讲稿）Chapter 03 Data Preprocessing

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major I asks in Data Preprocessing Data Cleaning a Data Integration Data reduction Data transformation and data discretization I Summary

2 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

Data Quality: Why Preprocess the Data? Measures for data quality A multidimensional view Accuracy: correct or wrong accurate or not Completeness: not recorded unavailable Consistency: some modified but some not, dangling Timeliness: timely update? Believability: how trustable the data are correct? Interpretability: how easily the data can be understood?

3 Data Quality: Why Preprocess the Data? ◼ Measures for data quality: A multidimensional view ◼ Accuracy: correct or wrong, accurate or not ◼ Completeness: not recorded, unavailable, … ◼ Consistency: some modified but some not, dangling, … ◼ Timeliness: timely update? ◼ Believability: how trustable the data are correct? ◼ Interpretability: how easily the data can be understood?

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

4 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 I asks in Data Preprocessing Data Cleaning a Data Integration Data reduction Data transformation and data discretization I Summary

5 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

Data Cleaning Data in the real World Is dirty: Lots of potentially incorrect data, e. g instrument faulty human or computer error transmission error incomplete lacking attribute values lacking certain attributes of interest, or containing only aggregate data e.g. Occupation="(missing data) noisy: containing noise, errors or outliers e.g. Salary--10"(an error) inconsistent: containing discrepancies in codes or names, e. g nA9e=42", Birthday=“03/07/2010″ Was rating"1, 2,3 now rating" A, B, c discrepancy between duplicate records Intentional(e.g. disguised missing data) Jan. 1 as everyone' s birthday

6 Data Cleaning ◼ Data in the Real World Is Dirty: Lots of potentially incorrect data, e.g., instrument faulty, human or computer error, transmission error ◼ incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data ◼ e.g., Occupation=“ ” (missing data) ◼ noisy: containing noise, errors, or outliers ◼ e.g., Salary=“−10” (an error) ◼ inconsistent: containing discrepancies in codes or names, e.g., ◼ Age=“42”, Birthday=“03/07/2010” ◼ Was rating “1, 2, 3”, now rating “A, B, C” ◼ discrepancy between duplicate records ◼ Intentional (e.g., disguised missing data) ◼ Jan. 1 as everyone’s birthday?

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

7 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

8 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

Noisy Data Noise: random error or variance in a measured variable Incorrect attribute values may be due to faulty data collection instruments data entry problems data transmission problems technology limitation inconsistency in naming convention Other data problems which require data cleaning duplicate records incomplete data inconsistent data

9 Noisy Data ◼ Noise: random error or variance in a measured variable ◼ Incorrect attribute values may be due to ◼ faulty data collection instruments ◼ data entry problems ◼ data transmission problems ◼ technology limitation ◼ inconsistency in naming convention ◼ Other data problems which require data cleaning ◼ duplicate records ◼ incomplete data ◼ inconsistent data

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 10

10 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. post 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) 11

11 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)