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
1 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
Why Data Preprocessing? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data a e.g. occupation= mm a noisy: containing errors or outliers aeg, salary=-10″ inconsistent: containing discrepancies in codes or names e.g. Age=42 Birthday=03/07/1997 e.g Was rating 1,213 now rating A, B,c e.g. discrepancy between duplicate records
2 Why Data Preprocessing? ◼ Data in the real world is dirty ◼ incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data ◼ e.g., occupation=“ ” ◼ noisy: containing errors or outliers ◼ e.g., Salary=“-10” ◼ inconsistent: containing discrepancies in codes or names ◼ e.g., Age=“42” Birthday=“03/07/1997” ◼ e.g., Was rating “1,2,3”, now rating “A, B, C” ◼ e.g., discrepancy between duplicate records
Why ls Data Dirty? incomplete data may come from Not applicable"data value when collected Different considerations between the time when the data was collected and when it is analyzed Human/hardware/software problems Noisy data(incorrect values) may come from Faulty data collection instruments Human or computer error at data entry Errors in data transmission Inconsistent data may come from Different data sources Functional dependency violation(e. g. modify some linked data) Duplicate records also need data cleaning
3 Why Is Data Dirty? ◼ Incomplete data may come from ◼ “Not applicable” data value when collected ◼ Different considerations between the time when the data was collected and when it is analyzed. ◼ Human/hardware/software problems ◼ Noisy data (incorrect values) may come from ◼ Faulty data collection instruments ◼ Human or computer error at data entry ◼ Errors in data transmission ◼ Inconsistent data may come from ◼ Different data sources ◼ Functional dependency violation (e.g., modify some linked data) ◼ Duplicate records also need data cleaning
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?
4 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
5 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