summarized best pre-processing techniques

April 1, 2025 Data Mining: Concepts and Techniqu 1
Data Mining:
Concepts and
Techniques
— Slides for Textbook —
— Chapter 3 —
©Jiawei Han and Micheline Kamber
Intelligent Database Systems Research Lab
School of Computing Science
Simon Fraser University, Canada
http://www.cs.sfu.ca

Chapter 3: Data Preprocessing
 Why preprocess the data?
 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
 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

Multi-Dimensional Measure of Data
Quality
 A well-accepted multidimensional view:
 Accuracy
 Completeness
 Consistency
 Timeliness
 Believability
 Value added
 Interpretability
 Accessibility
 Broad categories:
 intrinsic, contextual, representational, and
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 with particular importance,
especially for numerical data

Forms of data
preprocessing

 Data cleaning
 Data reduction
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 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
(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: 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 transmission problems
 technology limitation
 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
 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 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

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 (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

Cluster Analysis

Regression
x
y
y = x + 1
X1
Y1
Y1’

 Data cleaning
 Data reduction
generation
 Summary

Data Integration
 Data integration:
 combines 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
Data in Data Integration
 Redundant data occur often when integration of
multiple databases
 The same attribute may have different names in
different databases
 One attribute may be a “derived” attribute in
another table, e.g., annual revenue
 Redundant data may be able to be detected by
correlational analysis
 Careful integration of the data from multiple sources
may help reduce/avoid redundancies and
inconsistencies and improve mining speed and quality

Data Transformation
 Smoothing: remove noise from data
 Aggregation: summarization, data cube construction
 Generalization: concept hierarchy climbing
 Normalization: scaled to fall within a small, specified
range

min-max normalization
 z-score normalization
 normalization by decimal scaling
 Attribute/feature construction

New attributes constructed from the given ones

Data Transformation:
Normalization
 min-max normalization
 z-score normalization
 normalization by decimal scaling
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 Data cleaning
 Data reduction
generation
 Summary

Data Reduction Strategies
 Warehouse may store terabytes of data: Complex data
analysis/mining may take a very long time to run on the
complete data set
 Data reduction
 Obtains a reduced representation of the data set that
is much smaller in volume but yet produces the same
(or almost the same) analytical results
 Data reduction strategies
 Data cube aggregation
 Dimensionality reduction
 Numerosity reduction
 Discretization and concept hierarchy generation

Data Cube Aggregation
 The lowest level of a data cube
 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

Dimensionality Reduction
 Feature selection (i.e., attribute subset selection):

Select a minimum set of features such that the
probability distribution of different classes given the
values for those features is as close as possible to the
original distribution given the values of all features

reduce # of patterns in the patterns, easier to
understand
 Heuristic methods (due to exponential # of choices):

step-wise forward selection

step-wise backward elimination
 combining forward selection and backward elimination
 decision-tree induction

Example of Decision Tree Induction
Initial attribute set:
{A1, A2, A3, A4, A5, A6}
A4 ?
A1? A6?
Class 1 Class 2 Class 1 Class 2
> Reduced attribute set: {A1, A4, A6}

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

Data Compression
Original Data Compressed
Data
lossless
Original Data
Approximated
lossy

Wavelet Transforms
 Discrete wavelet transform (DWT): linear signal processing
 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 0s, 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
Haar2 Daubechie4

 Given N data vectors from k-dimensions, find c <= k
orthogonal vectors that can be best used to
represent data
 The original data set is reduced to one consisting
of N data vectors on c principal components
(reduced dimensions)
 Each data vector is a linear combination of the c
principal component vectors
 Works for numeric data only
 Used when the number of dimensions is large
Principal Component Analysis

X1
X2
Y1
Y2
Principal Component Analysis

Numerosity Reduction
 Parametric methods
 Assume the data fits some model, estimate
model parameters, store only the parameters,
and discard the data (except possible outliers)
 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

Regression and Log-Linear
Models
 Linear regression: Data are 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

April 1, 2025 Data Mining: Concepts and Techniqu
 Linear regression: Y =  +  X
 Two parameters ,  and  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:
 The multi-way table of joint probabilities is
approximated by a product of lower-order tables.

Probability: p(a, b, c, d) = ab acad bcd
Regress Analysis and Log-
Linear Models

Histograms
 A popular data
reduction technique
 Divide data into buckets
and store average (sum)
for each bucket
 Can be constructed
optimally in one
dimension using
dynamic programming
 Related to quantization
problems. 0
5
10
15
20
25
30
35
40
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Clustering
 Partition data set into clusters, and one can store
cluster representation 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, further detailed in Chapter 8

Sampling
 Allow a mining algorithm to run in complexity that is
potentially sub-linear to the size of the data
 Choose a representative subset of the data
 Simple random sampling may have very poor
performance in the presence of skew
 Develop adaptive sampling methods
 Stratified sampling:

Approximate the percentage of each class (or
subpopulation of interest) in the overall database

Used in conjunction with skewed data
 Sampling may not reduce database I/Os (page at a time).

Sampling
SRSWOR
(simple random
sample without
replacement)
SRSWR
Raw Data

Sampling
Raw Data Cluster/Stratified Sample

Hierarchical Reduction
 Use multi-resolution structure with different degrees
of reduction
 Hierarchical clustering is often performed but tends to
define partitions of data sets rather than “clusters”
 Parametric methods are usually not amenable to
hierarchical representation
 Hierarchical aggregation
 An index tree hierarchically divides a data set into
partitions by value range of some attributes
 Each partition can be considered as a bucket
 Thus an index tree with aggregates stored at each
node is a hierarchical histogram

 Data cleaning
 Data reduction
generation
 Summary

Discretization
 Three types of attributes:
 Nominal — values from an unordered set
 Ordinal — values from an ordered set
 Continuous — real numbers
 Discretization:
 divide the range of a continuous attribute into
intervals
 Some classification algorithms only accept
categorical attributes.
 Reduce data size by discretization
 Prepare for further analysis

Discretization and Concept
hierachy
 Discretization
 reduce the number of values for a given
continuous attribute by dividing the range of the
attribute into intervals. Interval labels can then be
used to replace actual data values.
 Concept hierarchies
 reduce the data by collecting and replacing low
level concepts (such as numeric values for the
attribute age) by higher level concepts (such as
young, middle-aged, or senior).

Discretization and concept
hierarchy generation for numeric
data
 Binning (see sections before)
 Histogram analysis (see sections before)
 Clustering analysis (see sections before)
 Entropy-based discretization
 Segmentation by natural partitioning

Entropy-Based Discretization
 Given a set of samples S, if S is partitioned into two
intervals S1 and S2 using boundary T, the entropy after
partitioning is
 The boundary that minimizes the entropy function over all
possible boundaries is selected as a binary discretization.
 The process is recursively applied to partitions obtained
until some stopping criterion is met, e.g.,
 Experiments show that it may reduce data size and
improve classification accuracy
E S T
S
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S
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| |
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| |
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2
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 

Segmentation by natural
partitioning
3-4-5 rule can be used to segment numeric data into
relatively uniform, “natural” intervals.
* If an interval covers 3, 6, 7 or 9 distinct values at the
most significant digit, partition the range into 3
equi-width intervals
* If it covers 2, 4, or 8 distinct values at the most
significant digit, partition the range into 4 intervals
* If it covers 1, 5, or 10 distinct values at the most
significant digit, partition the range into 5 intervals

Example of 3-4-5 rule
(-$4000 -$5,000)
(-$400 - 0)
(-$400 -
-$300)
(-$300 -
-$200)
(-$200 -
-$100)
(-$100 -
0)
(0 - $1,000)
(0 -
$200)
($200 -
$400)
($400 -
$600)
($600 -
$800) ($800 -
$1,000)
($2,000 - $5, 000)
($2,000 -
$3,000)
($3,000 -
$4,000)
($4,000 -
$5,000)
($1,000 - $2, 000)
($1,000 -
$1,200)
($1,200 -
$1,400)
($1,400 -
$1,600)
($1,600 -
$1,800)
($1,800 -
$2,000)
msd=1,000 Low=-$1,000 High=$2,000
Step 2:
Step 4:
Step 1: -$351 -$159 profit $1,838 $4,700
Min Low (i.e, 5%-tile) High(i.e, 95%-0 tile) Max
count
(-$1,000 - $2,000)
(-$1,000 - 0) (0 -$ 1,000)
Step 3:
($1,000 - $2,000)

Concept hierarchy generation for
categorical data
 Specification of a partial ordering of attributes
explicitly at the schema level by users or experts
 Specification of a portion of a hierarchy by explicit
data grouping
 Specification of a set of attributes, but not of their
partial ordering
 Specification of only a partial set of attributes

Specification of a set of attributes
Concept hierarchy can be automatically generated
based on the number of distinct values per
attribute in the given attribute set. The attribute
with the most distinct values is placed at the
lowest level of the hierarchy.
country
province_or_ state
city
street
15 distinct values
65 distinct
values
3567 distinct values
674,339 distinct values

 Data cleaning
 Data reduction
generation
 Summary

Summary
 Data preparation is a big issue for both warehousing
and mining
 Data preparation includes

Data cleaning and data integration

Data reduction and feature selection

Discretization
 A lot a methods have been developed but still an
active area of research

References
 D. P. Ballou and G. K. Tayi. Enhancing data quality in data warehouse
environments. Communications of ACM, 42:73-78, 1999.
 Jagadish et al., Special Issue on Data Reduction Techniques. Bulletin of
the Technical Committee on Data Engineering, 20(4), December 1997.
 D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999.
 T. Redman. Data Quality: Management and Technology. Bantam Books,
New York, 1992.
 Y. Wand and R. Wang. Anchoring data quality dimensions ontological
foundations. Communications of ACM, 39:86-95, 1996.
 R. Wang, V. Storey, and C. Firth. A framework for analysis of data quality
research. IEEE Trans. Knowledge and Data Engineering, 7:623-640, 1995.

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