数据挖掘课件-课件.ppt

Data Mining: Concepts and Techniques Data Preprocessing Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation 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 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 Is Data Dirty? Incomplete data comes from n/a data value when collected different consideration between the time when the data was collected and when it is analyzed. human/hardware/software problems Noisy data comes from the process of data collection entry transmission Inconsistent data comes from Different data sources Functional dependency violation Why Is Data Preprocessing Important? No quality data, no quality mining results! Quality decisions must be based on quality data e.g., duplicate or missing data may cause incorrect or even misleading statistics. Data warehouse needs consistent integration of quality data Data extraction, cleaning, and transformation comprises the majority of the work of building a data warehouse Multi-Dimensional Measure of Data Quality A well-accepted multidimensional view: Accuracy Completeness Consistency Timeliness Believability Interpretability Accessibility 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 transformation Normalization and aggregation Data reduction Obtains reduced representation in volume but produces the same or similar analytical results Data discretization Part of data reduction but