Data Preprocessing_17924109858fc09abd41bc880e540c13.ppt

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Data Preprocessing
 Data Preprocessing: An Overview
 Data Quality
 Major Tasks in Data Preprocessing
 Data Cleaning
 Data Integration
 Data Reduction
 Data Transformation and Data Discretization

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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?

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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

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Chapter 3: Data Preprocessing
 Data Quality
 Data Cleaning
 Data Reduction
 Summary

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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?

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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

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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: inference-based such as

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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

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How to Handle Noisy Data?
 Binning
 first sort data and partition into (equal-frequency)
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)

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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, spell-check) 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)

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 Data Quality
 Data Cleaning
 Data Reduction
 Summary

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Data Integration
 Data integration:
 Combines data from multiple sources into a coherent store
 Schema integration: e.g., A.cust-id  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

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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

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Correlation Analysis (Nominal Data)
 Χ2
(chi-square) test
 The larger the Χ2
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 car-theft in a city are correlated
 Both are causally linked to the third variable: population
χ2
=∑ (Observed−Expected )
2
Expected

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Chi-Square Calculation: An Example
 Χ2
(chi-square) calculation (numbers in parenthesis are
expected counts calculated based on the data
distribution in the two categories)
 It shows that like_science_fiction and play_chess are
correlated in the group
χ2
=
(250−90)
2
90
+
(50−210)
2
210
+
(200−360 )
2
360
+
(1000−840 )
2
840
=507 .93
Play
chess
Not play chess Sum (row)
Like science fiction 250(90) 200(360) 450
Not like science
fiction
50(210) 1000(840) 1050
Sum(col.) 300 1200 1500

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Correlation Analysis (Numeric Data)
 Correlation coefficient (also called Pearson’s product
moment coefficient)
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 cross-
product.
 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
rA, B=
∑i=1
n
(ai−A)(bi−B)
(n−1)σA σB
=
∑i=1
n
(ai bi)−n A B
(n−1)σA σB
A B

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Visually Evaluating Correlation
Scatter plots
showing the
similarity from
–1 to 1.

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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=(ak −mean( A ))/std ( A )
b 'k=(bk −mean(B ))/ std( B)
correlation ( A , B )= A '⋅B '

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Covariance (Numeric Data)
 Covariance is similar to correlation
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
A B
Correlation coefficient:

Co-Variance: 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.

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 Data Quality
 Data Cleaning
 Data Reduction
 Summary

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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 Log-Linear Models

Histograms, clustering, sampling

Data cube aggregation
 Data compression

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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)

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Mapping Data to a New Space
Two Sine Waves Two Sine Waves + Noise Frequency
 Fourier transform
 Wavelet transform

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What Is Wavelet Transform?
 Decomposes a signal into
different frequency
subbands
 Applicable to n-
dimensional 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

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Wavelet Transformation
 Discrete wavelet transform (DWT) for linear signal
processing, multi-resolution 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
Haar2 Daubechie4

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Wavelet Decomposition
 Wavelets: A math tool for space-efficient hierarchical
decomposition of functions
 S = [2, 2, 0, 2, 3, 5, 4, 4] can be transformed to S^ = [23
/4,
-11
/4, 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

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Haar Wavelet Coefficients
Coefficient
“Supports”
2 2 0 2 3 5 4 4
-1.25
2.75
0.5 0
0 -1 0
-1
+
-
+
+
+ + +
+
+
- -
- - - -
+
-
+
+
-
+
-
+
-+
-
-
+
+
-
-1
-1
0.5
0
2.75
-1.25
0
0
Original frequency distribution
Hierarchical
decomposition
structure (a.k.a.
“error tree”)

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Why Wavelet Transform?
 Use hat-shape filters
 Emphasize region where points cluster
 Suppress weaker information in their boundaries
 Effective removal of outliers
 Insensitive to noise, insensitive to input order
 Multi-resolution
 Detect arbitrary shaped clusters at different scales
 Efficient
 Complexity O(N)
 Only applicable to low dimensional data

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x2
x1
e
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

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 Given N data vectors from n-dimensions, 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
Principal Component Analysis (Steps)

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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

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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 step-wise feature selection:

The best single-attribute is picked first

Then next best attribute condition to the first, ...
 Step-wise attribute elimination:

Repeatedly eliminate the worst attribute
 Best combined attribute selection and elimination
 Optimal branch and bound:

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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

Domain-specific
 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)

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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.: Log-linear models—obtain value at a point in
m-D space as the product on appropriate marginal
subspaces
 Non-parametric methods
 Do not assume models
 Major families: histograms, clustering, sampling, …

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Parametric Data Reduction: Regression
and Log-Linear Models
 Linear regression
 Data modeled to fit a straight line
 Often uses the least-square method to fit the line
 Multiple regression
 Allows a response variable Y to be modeled as a
linear function of multidimensional feature vector
 Log-linear model
 Approximates discrete multidimensional
probability distributions

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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
time-series data),
inference, hypothesis
testing, and modeling of
causal relationships
y
x
y = x + 1
X1
Y1
Y1’

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 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 = b0 + b1 X1 + b2 X2
 Many nonlinear functions can be transformed into the above
 Log-linear models:
 Approximate discrete multidimensional probability
distributions
 Estimate the probability of each point (tuple) in a multi-
dimensional space for a set of discretized attributes, based on a
smaller subset of dimensional combinations
Regress Analysis and Log-Linear Models

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Histogram Analysis
 Divide data into buckets and
store average (sum) for each
bucket
 Partitioning rules:
 Equal-width: equal bucket
range
 Equal-frequency (or
equal-depth)
0
5
10
15
20
25
30
35
40
1
0
0
0
0
2
0
0
0
0
3
0
0
0
0
4
0
0
0
0
5
0
0
0
0
6
0
0
0
0
7
0
0
0
0
8
0
0
0
0
9
0
0
0
0
1
0
0
0
0
0

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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
multi-dimensional index tree structures
 There are many choices of clustering definitions and
clustering algorithms
 Cluster analysis will be studied in depth in Chapter 10

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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 sub-linear 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

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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

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Sampling: With or without Replacement
SRSWOR
(simple random
sample without
replacement)
SRSWR
Raw Data

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Sampling: Cluster or Stratified Sampling
Raw Data Cluster/Stratified Sample

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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

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Data Reduction 3: Data Compression
 String compression
 There are extensive theories and well-tuned
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

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Data Compression
Original Data Compressed
Data
lossless
Original Data
Approximated
lossy

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 Data Quality
 Data Cleaning
 Data Reduction
 Summary

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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

min-max normalization

z-score normalization

normalization by decimal scaling
 Discretization: Concept hierarchy climbing

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Normalization
 Min-max normalization: to [new_minA, new_maxA]
 Ex. Let income range $12,000 to $98,000 normalized to [0.0,
1.0]. Then $73,000 is mapped to
 Z-score normalization (μ: mean, σ: standard deviation):
 Ex. Let μ = 54,000, σ = 16,000. Then
 Normalization by decimal scaling
73,600−12,000
98,000−12,000
(1.0−0)+0=0.716
v '=
v−min A
max A−min A
(newmax A−newmin A )+newmin A
v '=
v−μ A
σ A
v '=
v
10
j Where j is the smallest integer such that Max(|ν’|) < 1
73,600−54,000
16,000
=1.225

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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 (top-down) vs. merge (bottom-up)
 Discretization can be performed recursively on an attribute
 Prepare for further analysis, e.g., classification

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Data Discretization Methods
 Typical methods: All the methods can be applied
recursively
 Binning

Top-down split, unsupervised
 Histogram analysis

Top-down split, unsupervised
 Clustering analysis (unsupervised, top-down split or
bottom-up merge)
 Decision-tree analysis (supervised, top-down split)
 Correlation (e.g., 2
) analysis (unsupervised, bottom-
up merge)

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Simple Discretization: Binning
 Equal-width (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
 Equal-depth (frequency) partitioning
 Divides the range into N intervals, each containing
approximately same number of samples
 Good data scaling
 Managing categorical attributes can be tricky

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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 equal-frequency (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, 25, 25
- Bin 3: 26, 26, 26, 34

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Discretization Without Using Class Labels
(Binning vs. Clustering)
Data Equal interval width
(binning)
Equal frequency (binning) K-means clustering leads to better
results

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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)
 Top-down, recursive split
 Details to be covered in Chapter 7
 Correlation analysis (e.g., Chi-merge: χ2
-based discretization)
 Supervised: use class information
 Bottom-up 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

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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.

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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}

60
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
country
province_or_ state
city
street
15 distinct values
365 distinct values
3567 distinct values
674,339 distinct values

Data Preprocessing_17924109858fc09abd41bc880e540c13.ppt

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