Data preprocessing ppt

data preprocessing in data warehouse ppt and data preprocessing techniques ppt
Dr.JakeFinlay Profile Pic
Dr.JakeFinlay,Germany,Teacher
Published Date:22-07-2017
Your Website URL(Optional)
Comment
Data Preprocessing www.ThesisScientist.comData Preprocessing  Why preprocess the data?  Data cleaning  Data integration and transformation  Data reduction  Discretization and concept hierarchy generation  Summary www.ThesisScientist.comWhy Data Preprocessing?  Data in the 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 based on quality data  Data warehouse needs consistent integration of quality data  Required for both OLAP and Data Mining www.ThesisScientist.comWhy can Data be Incomplete?  Attributes of interest are not available (e.g., customer information for sales transaction data)  Data were not considered important at the time of transactions, so they were not recorded  Data not recorder because of misunderstanding or malfunctions  Data may have been recorded and later deleted  Missing/unknown values for some data www.ThesisScientist.comWhy can Data be Noisy/Inconsistent?  Faulty instruments for data collection  Human or computer errors  Errors in data transmission  Technology limitations (e.g., sensor data come at a faster rate than they can be processed)  Inconsistencies in naming conventions or data codes (e.g., 2/5/2002 could be 2 May 2002 or 5 Feb 2002)  Duplicate tuples, which were received twice should also be removed www.ThesisScientist.comMajor Tasks in Data Preprocessing outliers=exceptions  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 with particular importance, especially for numerical data www.ThesisScientist.comForms of data preprocessing www.ThesisScientist.comData Preprocessing  Why preprocess the data?  Data cleaning  Data integration and transformation  Data reduction  Discretization and concept hierarchy generation www.ThesisScientist.com  SummaryData Cleaning  Data cleaning tasks  Fill in missing values  Identify outliers and smooth out noisy data  Correct inconsistent data www.ThesisScientist.comHow 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 varies 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 missing value: smarter  Use the most probable value to fill in the missing value: www.ThesisScientist.com inference-based such as Bayesian formula or decision treeHow to Handle Missing Data? Age Income Religion Gender 23 24,200 Muslim M 39 ? Christian F 45 45,390 ? F Fill missing values using aggregate functions (e.g., average) or probabilistic estimates on global value distribution E.g., put the average income here, or put the most probable income based on the fact that the person is 39 years old E.g., put the most frequent religion here www.ThesisScientist.comNoisy Data  Noise: random error or variance in a measured variable  Incorrect attribute values may exist due to  faulty data collection instruments  data entry problems  data transmission problems  technology limitation  inconsistency in naming convention  Other data problems which requires data cleaning  duplicate records  incomplete data  inconsistent data www.ThesisScientist.comHow to Handle Noisy Data? Smoothing techniques  Binning method:  first sort data and partition into (equi-depth) bins  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  computer detects suspicious values, which are then checked by humans  Regression  smooth by fitting the data into regression functions  Use Concept hierarchies  use concept hierarchies, e.g., price value - ―expensive‖ www.ThesisScientist.comSimple Discretization Methods: 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 not handled well.  Equal-depth (frequency) partitioning:  It divides the range into N intervals, each containing approximately same number of samples  Good data scaling – good handing of skewed data www.ThesisScientist.comSimple Discretization Methods: Binning number Example: customer ages of values Equi-width binning: 30-40 20-30 40-50 50-60 60-70 70-80 10-20 0-10 Equi-width binning: 22-31 62-80 0-22 48-55 38-44 www.ThesisScientist.com 55-62 32-38 44-48Smoothing using Binning Methods Sorted data for price (in dollars): 4, 8, 9, 15, 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: 4,15,21,25,26,34 - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34 www.ThesisScientist.comCluster Analysis salary cluster outlier age www.ThesisScientist.comRegression y (salary) Example of linear regression Y1 y = x + 1 x X1 (age) www.ThesisScientist.comInconsistent Data  Inconsistent data are handled by:  Manual correction (expensive and tedious)  Use routines designed to detect inconsistencies and manually correct them. E.g., the routine may use the check global constraints (age10) or functional dependencies  Other inconsistencies (e.g., between names of the same attribute) can be corrected during the data integration process www.ThesisScientist.comData Preprocessing  Why preprocess the data?  Data cleaning  Data integration and transformation  Data reduction  Discretization and concept hierarchy generation  Summary www.ThesisScientist.com