105221" datareactid="8">
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
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?
Incomplete (Missing) Data
 Data is not always available
 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 important 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 (when doing classification)—not effective when the % of missing values per attribute varies considerably
 Fill in the missing value manually: tedious + infeasible?
 Fill in it automatically with
 a global constant : e.g., “unknown”, a new class?!
 the attribute mean
 the attribute mean for all samples belonging to the same class: smarter
 the most probable value: inferencebased such as Bayesian formula or decision tree
Noisy Data
 Noise: random error or variance in a measured variable
 Incorrect attribute values may be due to
 faulty data collection instruments
 data entry problems
 data transmission problems
 technology limitation
 inconsistency in naming convention
 Other data problems which require data cleaning
 duplicate records
 incomplete data
 inconsistent data
How to Handle Noisy Data?
 Binning
 first sort data and partition into (equalfrequency) bins
 then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.
 Regression
 smooth by fitting the data into regression functions
 Clustering
 detect and remove outliers
 Combined computer and human inspection
 detect suspicious values and check by human (e.g., deal with possible outliers)
Data Cleaning as a Process
 Data discrepancy detection
 Use metadata (e.g., domain, range, dependency, distribution)
 Check field overloading
 Check uniqueness rule, consecutive rule and null rule
 Use commercial tools
 Data scrubbing: use simple domain knowledge (e.g., postal code, spellcheck) to detect errors and make corrections
 Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers)
 Data migration and integration
 Data migration tools: allow transformations to be specified
 ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface
 Integration of the two processes
 Iterative and interactive (e.g., Potter’s Wheels)
Data Integration
 Combines data from multiple sources into a coherent store
 Schema integration: e.g., A.custid ≡ B.cust#
 Integrate metadata from different sources
 Entity identification problem:
 Identify real world entities from multiple data sources, e.g., Bill Clinton = William Clinton
 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 Redundancy in Data Integration
 Redundant data occur often when integration of multiple databases
 Object identification: The same attribute or object may have different names in different databases
 Derivable data: One attribute may be a “derived” attribute in another table, e.g., annual revenue
 Redundant attributes may be able to be detected by correlation analysis and covariance analysis
 Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality
Correlation Analysis (Nominal Data)
\[ X^{2}=\sum \frac{(ObservedExpected)^2}{Expected} \]
 The larger the Χ^2 value, the more likely the variables are related
 The cells that contribute the most to the Χ2 value are those whose actual count is very different from the expected count
 Correlation does not imply causality
 # of hospitals and # of cartheft in a city are correlated
 Both are causally linked to the third variable: population
ChiSquare Calculation: An Example
 X^2 (chisquare) calculation (numbers in parenthesis are expected counts calculated based on the data distribution in the two categories)
\[ X^{2}=\frac{(25090)^2}{90} + \frac{(50210)^2}{210} + \frac{(200360)^2}{360} + \frac{(1000840)^2} {840} = 507.93 \]

It shows that like_science_fiction and play_chess are correlated in the group
Correlation Analysis (Numeric Data)
 Correlation coefficient (also called Pearson’s product moment coefficient)
\[ {r_{A,B}}=\frac{\sum_{i=1}^{n} (a_{i}\bar{A})
(b_{i}\bar{B})}{(n1)\sigma_{A} \sigma_{B}}=\frac{\sum_{i=1}^{n}
(a_{i}b_{i})n \bar{A}\bar{B}}{(n1)\sigma_{A} \sigma_{B}} \]
where n is the number of tuples, and are the respective means of A and B, σA and σB are the respective standard deviation of A and B, and Σ(aibi) is the sum of the AB crossproduct.  If rA,B > 0, A and B are positively correlated (A’s values increase as B’s). The higher, the stronger correlation.
 rA,B = 0: independent; rAB < 0: negatively correlated
Visually Evaluating Correlation
Correlation (viewed as linear relationship)
 Correlation measures the linear relationship between objects
 To compute correlation, we standardize data objects, A and B, and then take their dot product
\[ a^{'}_{k} = (a_{k}mean(A))/std(A) \]
\[ b^{'}_{k} = (b_{k}mean(B))/std(B) \]
\[ correlation (A,B)=A^{'}.B^{'} \]
Covariance (Numeric Data)
 Covariance is similar to correlation
Correlation coefficient:
where n is the number of tuples, and are the respective mean or expected values of A and B, σA and σB are the respective standard deviation of A and B.
 Positive covariance: If CovA,B > 0, then A and B both tend to be larger than their expected values.
 Negative covariance: If CovA,B < 0 then if A is larger than its expected value, B is likely to be smaller than its expected value.
 Independence: CovA,B = 0 but the converse is not true:
 Some pairs of random variables may have a covariance of 0 but are not independent. Only under some additional assumptions (e.g., the data follow multivariate normal distributions) does a covariance of 0 imply independence
CoVariance: An Example
 It can be simplified in computation as
 Suppose two stocks A and B have the following values in one week: (2, 5), (3, 8), (5, 10), (4, 11), (6, 14).
 Question: If the stocks are affected by the same industry trends, will their prices rise or fall together?
 E(A) = (2 + 3 + 5 + 4 + 6)/ 5 = 20/5 = 4
 E(B) = (5 + 8 + 10 + 11 + 14) /5 = 48/5 = 9.6
 Cov(A,B) = (2×5+3×8+5×10+4×11+6×14)/5 − 4 × 9.6 = 4
 Thus, A and B rise together since Cov(A, B) > 0.
Data Reduction Strategies
 Data reduction: Obtain a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results
 Why data reduction? — A database/data warehouse may store terabytes of data. Complex data analysis may take a very long time to run on the complete data set.
 Data reduction strategies
 Dimensionality reduction, e.g., remove unimportant attributes
 Wavelet transforms
 Principal Components Analysis (PCA)
 Feature subset selection, feature creation
 Numerosity reduction (some simply call it: Data Reduction)
 Regression and LogLinear Models
 Histograms, clustering, sampling
 Data cube aggregation
 Data compression
Data Reduction 1: Dimensionality Reduction
 Curse of dimensionality
 When dimensionality increases, data becomes increasingly sparse
 Density and distance between points, which is critical to clustering, outlier analysis, becomes less meaningful
 The possible combinations of subspaces will grow exponentially
 Dimensionality reduction
 Avoid the curse of dimensionality
 Help eliminate irrelevant features and reduce noise
 Reduce time and space required in data mining
 Allow easier visualization
 Dimensionality reduction techniques
 Wavelet transforms
 Principal Component Analysis
 Supervised and nonlinear techniques (e.g., feature selection)
Mapping Data to a New Space
 Fourier transform
 Wavelet transform
What Is Wavelet Transform?
 Decomposes a signal into different frequency subbands
 Applicable to ndimensional signals
 Data are transformed to preserve relative distance between objects at different levels of resolution
 Allow natural clusters to become more distinguishable
 Used for image compression
Wavelet Transformation
 Discrete wavelet transform (DWT) for linear signal processing, multiresolution analysis
 Compressed approximation: store only a small fraction of the strongest of the wavelet coefficients
 Similar to discrete Fourier transform (DFT), but better lossy compression, localized in space
 Method:
 Length, L, must be an integer power of 2 (padding with 0’s, when necessary)
 Each transform has 2 functions: smoothing, difference
 Applies to pairs of data, resulting in two set of data of length L/2
 Applies two functions recursively, until reaches the desired length
Wavelet Decomposition
 Wavelets: A math tool for spaceefficient hierarchical decomposition of functions
 S = [2, 2, 0, 2, 3, 5, 4, 4] can be transformed to
\[ S^ = [2\frac{3}{4}, 1\frac{1}{4}, \frac{1}{2}, 0, 0, 1, 1, 0] \]
 Compression: many small detail coefficients can be replaced by 0’s, and only the significant coefficients are retained
Why Wavelet Transform?
 Use hatshape filters
 Emphasize region where points cluster
 Suppress weaker information in their boundaries
 Effective removal of outliers
 Insensitive to noise, insensitive to input order
 Multiresolution
 Detect arbitrary shaped clusters at different scales
 Efficient
 Only applicable to low dimensional data
Principal Component Analysis (PCA)
 Find a projection that captures the largest amount of variation in data
 The original data are projected onto a much smaller space, resulting in dimensionality reduction. We find the eigenvectors of the covariance matrix, and these eigenvectors define the new space
 Given N data vectors from ndimensions, find k ≤ n orthogonal vectors (principal components) that can be best used to represent data
 Normalize input data: Each attribute falls within the same range
 Compute k orthonormal (unit) vectors, i.e., principal components
 Each input data (vector) is a linear combination of the k principal component vectors
 The principal components are sorted in order of decreasing “significance” or strength
 Since the components are sorted, the size of the data can be reduced by eliminating the weak components, i.e., those with low variance (i.e., using the strongest principal components, it is possible to reconstruct a good approximation of the original data)
 Works for numeric data only
Principal Component Analysis (Steps)
Attribute Subset Selection
 Another way to reduce dimensionality of data
 Redundant attributes
 Duplicate much or all of the information contained in one or more other attributes
 E.g., purchase price of a product and the amount of sales tax paid
 Irrelevant attributes
 Contain no information that is useful for the data mining task at hand
 E.g., students' ID is often irrelevant to the task of predicting students' GPA
Heuristic Search in Attribute Selection
 There are 2d possible attribute combinations of d attributes
 Typical heuristic attribute selection methods:
 Best single attribute under the attribute independence assumption: choose by significance tests
 Best stepwise feature selection:
 The best singleattribute is picked first
 Then next best attribute condition to the first, ...
 Stepwise attribute elimination:
 Repeatedly eliminate the worst attribute
 Best combined attribute selection and elimination
 Optimal branch and bound:
 Use attribute elimination and backtracking
Attribute Creation (Feature Generation)
 Create new attributes (features) that can capture the important information in a data set more effectively than the original ones
 Three general methodologies
 Attribute extraction
 Mapping data to new space (see: data reduction)
 E.g., Fourier transformation, wavelet transformation, manifold approaches (not covered)
 Attribute construction
 Combining features (see: discriminative frequent patterns in Chapter 7)
 Data discretization
Data Reduction 2: Numerosity Reduction
 Reduce data volume by choosing alternative, smaller forms of data representation
 Parametric methods (e.g., regression)
 Assume the data fits some model, estimate model parameters, store only the parameters, and discard the data (except possible outliers)
 Ex.: Loglinear models—obtain value at a point in mD space as the product on appropriate marginal subspaces
 Nonparametric methods
 Do not assume models
 Major families: histograms, clustering, sampling, …
Parametric Data Reduction: Regression and LogLinear Models
 Linear regression
 Data modeled to fit a straight line
 Often uses the leastsquare method to fit the line
 Multiple regression
 Allows a response variable Y to be modeled as a linear function of multidimensional feature vector
 Loglinear model
 Approximates discrete multidimensional probability distributions
Regression Analysis
 Regression analysis: A collective name for techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (also called response variable or measurement) and of one or more independent variables (aka. explanatory variables or predictors)
 The parameters are estimated so as to give a "best fit" of the data
 Most commonly the best fit is evaluated by using the least squares method, but other criteria have also been used
 Used for prediction (including forecasting of timeseries data), inference, hypothesis testing, and modeling of causal relationships
 Linear regression: Y = w X + b
 Two regression coefficients, w and b, specify the line and are to be estimated by using the data at hand
 Using the least squares criterion to the known values of Y1, Y2, …, X1, X2, ….
 Multiple regression: Y = b + b1 X1 + b2 X2
 Many nonlinear functions can be transformed into the above
 Loglinear models:
 Approximate discrete multidimensional probability distributions
 Estimate the probability of each point (tuple) in a multidimensional space for a set of discretized attributes, based on a smaller subset of dimensional combinations
 Useful for dimensionality reduction and data smoothing
Regress Analysis and LogLinear Models
Histogram Analysis
 Divide data into buckets and store average (sum) for each bucket
 Partitioning rules:
 Equalwidth: equal bucket range
 Equalfrequency (or equaldepth)
Clustering
 Partition data set into clusters based on similarity, and store cluster representation (e.g., centroid and diameter) only
 Can be very effective if data is clustered but not if data is “smeared”
 Can have hierarchical clustering and be stored in multidimensional index tree structures
 There are many choices of clustering definitions and clustering algorithms
 Cluster analysis will be studied in depth in Chapter 10
Sampling
 Sampling: obtaining a small sample s to represent the whole data set N
 Allow a mining algorithm to run in complexity that is potentially sublinear to the size of the data
 Key principle: Choose a representative subset of the data
 Simple random sampling may have very poor performance in the presence of skew
 Develop adaptive sampling methods, e.g., stratified sampling:
 Note: Sampling may not reduce database I/Os (page at a time)
Types of Sampling
 Simple random sampling
 There is an equal probability of selecting any particular item
 Sampling without replacement
 Once an object is selected, it is removed from the population
 Sampling with replacement
 A selected object is not removed from the population
 Stratified sampling:
 Partition the data set, and draw samples from each partition (proportionally, i.e., approximately the same percentage of the data)
 Used in conjunction with skewed data
Sampling: With or without Replacement
Sampling: Cluster or Stratified Sampling
Data Cube Aggregation
 The lowest level of a data cube (base cuboid)
 The aggregated data for an individual entity of interest
 E.g., a customer in a phone calling data warehouse
 Multiple levels of aggregation in data cubes
 Further reduce the size of data to deal with
 Reference appropriate levels
 Use the smallest representation which is enough to solve the task
 Queries regarding aggregated information should be answered using data cube, when possible
Data Reduction 3: Data Compression
 String compression
 There are extensive theories and welltuned algorithms
 Typically lossless, but only limited manipulation is possible without expansion
 Audio/video compression
 Typically lossy compression, with progressive refinement
 Sometimes small fragments of signal can be reconstructed without reconstructing the whole
 Time sequence is not audio
 Typically short and vary slowly with time
 Dimensionality and numerosity reduction may also be considered as forms of data compression
Data Compression
Data Transformation
 A function that maps the entire set of values of a given attribute to a new set of replacement values s.t. each old value can be identified with one of the new values
 Methods
 Smoothing: Remove noise from data
 Attribute/feature construction
 New attributes constructed from the given ones
 Aggregation: Summarization, data cube construction
 Normalization: Scaled to fall within a smaller, specified range
 minmax normalization
 zscore normalization
 normalization by decimal scaling
 Discretization: Concept hierarchy climbing
Normalization
 Minmax normalization: to [new_minA, new_maxA]
\[ v^{'}=\frac{vmin_{A}}{max_{A}min_{A}}(newmax_{A}  newmin_{A})+ newmin_{A} \]
 Ex. Let income range $12,000 to $98,000 normalized to [0.0, 1.0]. Then $73,000 is mapped to
\[ \frac{73,60012,000}{98,00012,000}(1.0  0)+ 0 = 0.716 \]
 Zscore normalization (μ: mean, σ: standard deviation):
\[ v^{'} = \frac{v\mu_{A}}{\sigma _{A}} \]
 Ex. Let μ = 54,000, σ = 16,000. Then
\[ \frac{73,60054,000}{16,000} = 1.225 \]
 Normalization by decimal scaling
\[ v^{'} = \frac{v}{10^{j}} \]
Where j is the smallest integer such that Max(ν’) < 1
Discretization
 Three types of attributes
 Nominal—values from an unordered set, e.g., color, profession
 Ordinal—values from an ordered set, e.g., military or academic rank
 Numeric—real numbers, e.g., integer or real numbers
 Discretization: Divide the range of a continuous attribute into intervals
 Interval labels can then be used to replace actual data values
 Reduce data size by discretization
 Supervised vs. unsupervised
 Split (topdown) vs. merge (bottomup)
 Discretization can be performed recursively on an attribute
 Prepare for further analysis, e.g., classification
Data Discretization Methods
 Typical methods: All the methods can be applied recursively
 Binning
 Topdown split, unsupervised
 Histogram analysis
 Topdown split, unsupervised
 Clustering analysis (unsupervised, topdown split or bottomup merge)
 Decisiontree analysis (supervised, topdown split)
 Correlation (e.g., X^2) analysis (unsupervised, bottomup merge)
Simple Discretization: Binning
 Equalwidth (distance) partitioning
 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
 Equaldepth (frequency) partitioning
 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, 21, 21, 24, 25, 26, 28, 29, 34
 * Partition into equalfrequency (equidepth) 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, 25, 25
  Bin 3: 26, 26, 26, 34
Discretization Without Using Class Labels(Binning vs. Clustering)
Discretization by Classification & Correlation Analysis
 Classification (e.g., decision tree analysis)
 Supervised: Given class labels, e.g., cancerous vs. benign
 Using entropy to determine split point (discretization point)
 Topdown, recursive split
 Details to be covered in Chapter 7
 Correlation analysis (e.g., Chimerge: χ2based discretization)
 Supervised: use class information
 Bottomup merge: find the best neighboring intervals (those having similar distributions of classes, i.e., low χ2 values) to merge
 Merge performed recursively, until a predefined stopping condition
Concept Hierarchy Generation
 Concept hierarchy organizes concepts (i.e., attribute values) hierarchically and is usually associated with each dimension in a data warehouse
 Concept hierarchies facilitate drilling and rolling in data warehouses to view data in multiple granularity
 Concept hierarchy formation: Recursively reduce the data by collecting and replacing low level concepts (such as numeric values for age) by higher level concepts (such as youth, adult, or senior)
 Concept hierarchies can be explicitly specified by domain experts and/or data warehouse designers
 Concept hierarchy can be automatically formed for both numeric and nominal data. For numeric data, use discretization methods shown.
Concept Hierarchy Generation for Nominal Data
 Specification of a partial/total ordering of attributes explicitly at the schema level by users or experts
 street < city < state < country
 Specification of a hierarchy for a set of values by explicit data grouping
 {Urbana, Champaign, Chicago} < Illinois
 Specification of only a partial set of attributes
 E.g., only street < city, not others
 Automatic generation of hierarchies (or attribute levels) by the analysis of the number of distinct values
 E.g., for a set of attributes: {street, city, state, country}
Automatic Concept Hierarchy Generation
 Some hierarchies can be automatically generated based on the analysis of the number of distinct values per attribute in the data set
 The attribute with the most distinct values is placed at the lowest level of the hierarchy
 Exceptions, e.g., weekday, month, quarter, year
Summary
 Data quality: accuracy, completeness, consistency, timeliness, believability, interpretability
 Data cleaning: e.g. missing/noisy values, outliers
 Data integration from multiple sources:
 Entity identification problem
 Remove redundancies
 Detect inconsistencies
 Data reduction
 Dimensionality reduction
 Numerosity reduction
 Data compression
 Data transformation and data discretization
 Normalization
 Concept hierarchy generation
References
 D. P. Ballou and G. K. Tayi. Enhancing data quality in data warehouse environments. Comm. of ACM, 42:7378, 1999
 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley, 2003
 T. Dasu, T. Johnson, S. Muthukrishnan, V. Shkapenyuk. Mining Database Structure; Or, How to Build a Data Quality Browser. SIGMOD’02
 H. V. Jagadish et al., Special Issue on Data Reduction Techniques. Bulletin of the Technical Committee on Data Engineering, 20(4), Dec. 1997
 D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999
 E. Rahm and H. H. Do. Data Cleaning: Problems and Current Approaches. IEEE Bulletin of the Technical Committee on Data Engineering. Vol.23, No.4
 V. Raman and J. Hellerstein. Potters Wheel: An Interactive Framework for Data Cleaning and Transformation, VLDB’2001
 T. Redman. Data Quality: Management and Technology. Bantam Books, 1992
 R. Wang, V. Storey, and C. Firth. A framework for analysis of data quality research. IEEE Trans. Knowledge and Data Engineering, 7:623640, 1995