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?