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1、Data Preprocessing,School of Software, Nanjing University,Knowledge Discovery in Databases,Chapter 3: Data Preprocessing,Why data preprocessing? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary,Why Data Preprocessing?,Data in th
2、e real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data noisy: containing errors or outliers inconsistent: containing discrepancies in codes or names No quality data, no quality mining results! Quality decisions must be ba
3、sed on quality data Data warehouse needs consistent integration of quality data,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
4、 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 with particular importance, especially for numerical data,Chapter 3: Data Preprocessing,Why da
5、ta preprocessing? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary,Data Cleaning,Data cleaning tasks Fill in missing values Identify outliers and smooth out noisy data Correct inconsistent data,Missing Data,Data is not always av
6、ailable 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 importa
7、nt 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 (assuming the tasks in classification) not effective when the percentage of missing values per attribute va
8、ries considerably. Fill in the missing value manually: tedious + infeasible? Use a global constant to fill in the missing value: e.g., “unknown”, a new class?! Use the attribute mean to fill in the missing value Use the attribute mean for all samples belonging to the same class to fill in the missin
9、g value: smarter Use the most probable value to fill in the missing 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 due to faulty data collection instruments data entry problems data tra
10、nsmission problems technology limitation(e.g. Input cache capacity ) inconsistency in naming convention Other data problems which requires data cleaning duplicate records incomplete data inconsistent data,How to Handle Noisy Data?,Binning method: first sort data and partition into (equi-depth) bins
11、then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. Clustering detect and remove outliers Combined computer and human inspection detect suspicious values and check by human Regression smooth by fitting the data into regression functions,Simple Discretization Method
12、s: Binning,Equal-width (distance) partitioning: It divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. The most straightforward But outliers may dominate presentation Skewed data is
13、 not handled well. Equal-depth (frequency) partitioning: It divides the range into N intervals, each containing approximately same number of samples Good data scaling Managing categorical attributes can be tricky.,Binning Methods for Data Smoothing,* Sorted data for price (in dollars): 4, 8, 9, 15,
14、21, 21, 24, 25, 26, 28, 29, 34 * Partition into (equi-depth) bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34 * Smoothing by bin means: - Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29 * Smoothing by bin boundaries: - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 2
15、5, 25 - Bin 3: 26, 26, 26, 34,Cluster Analysis,Regression,x,y,y = x + 1,X1,Y1,Y1,Chapter 3: Data Preprocessing,Why data preprocessing? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary,Data Integration,Data integration: combines
16、data from multiple sources into a coherent store Schema integration integrate metadata from different sources Entity identification problem: identify real world entities from multiple data sources, e.g., A.cust-id B.cust-# 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 Redundant D
