Pre_processing_the_data_using_advance_technique

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Data Mining:
Concepts and Techniques
— Chapter 3 —

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Chapter 3: Data Preprocessing
 Why preprocess the data?
 Descriptive data summarization
 Data cleaning
 Data integration and transformation
 Data reduction
 Discretization and concept hierarchy generation
 Summary

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Why Data Preprocessing?
 Data in the real world is dirty
 incomplete: lacking attribute values, lacking
certain attributes of interest, or containing
only aggregate data
 e.g., occupation=“ ”
 noisy: containing errors or outliers
 e.g., Salary=“-10”
 inconsistent: containing discrepancies in codes
or names
 e.g., Age=“42” Birthday=“03/07/1997”
 e.g., Was rating “1,2,3”, now rating “A, B, C”
 e.g., discrepancy between duplicate records

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Why Is Data Dirty?
 Incomplete data may come from
 “Not applicable” data value when collected
 Different considerations between the time when the data was
collected and when it is analyzed.
 Human/hardware/software problems
 Noisy data (incorrect values) may come from
 Faulty data collection instruments
 Human or computer error at data entry
 Errors in data transmission
 Inconsistent data may come from
 Different data sources
 Functional dependency violation (e.g., modify some linked data)
 Duplicate records also need data cleaning

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Why Is Data Preprocessing Important?
 No quality data, no quality mining results!
 Quality decisions must be based on quality data
 e.g., duplicate or missing data may cause incorrect or even
misleading statistics.
 Data warehouse needs consistent integration of quality
data
 Data extraction, cleaning, and transformation comprises
the majority of the work of building a data warehouse

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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 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

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 Data cleaning
 Data reduction
 Summary

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Mining Data Descriptive Characteristics
 Motivation
 To better understand the data: central tendency, variation
and spread
 Data dispersion characteristics
 median, max, min, quantiles, outliers, variance, etc.
 Numerical dimensions correspond to sorted intervals
 Data dispersion: analyzed with multiple granularities of
precision
 Boxplot or quantile analysis on sorted intervals
 Dispersion analysis on computed measures
 Folding measures into numerical dimensions
 Boxplot or quantile analysis on the transformed cube

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Measuring the Central Tendency
 Mean (algebraic measure) (sample vs. population):
 Weighted arithmetic mean:
 Trimmed mean: chopping extreme values
 Median: A holistic measure
 Middle value if odd number of values, or average of the middle two
values otherwise
 Estimated by interpolation (for grouped data):
 Mode
 Value that occurs most frequently in the data
 Unimodal, bimodal, trimodal
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58 60 61
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Symmetric vs. Skewed Data
 Median, mean and mode of
symmetric, positively and
negatively skewed data

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Measuring the Dispersion of Data
 Quartiles, outliers and boxplots
 Quartiles: Q1 (25th percentile), Q3 (75th percentile)
 Inter-quartile range: IQR = Q3 – Q1
 Five number summary: min, Q1, M, Q3, max
 Boxplot: ends of the box are the quartiles, median is marked, and plot
outlier individually
 Variance and standard deviation (sample: s, population: σ)
 Variance: (algebraic, scalable computation)
 Standard deviation s (or σ) is the square root of variance s2 (or σ2)
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Boxplot Analysis
 Five-number summary of a distribution:
Minimum, Q1, M, Q3, Maximum
 Boxplot
 Data is represented with a box
 The ends of the box are at the first and third
quartiles, i.e., the height of the box is IRQ
 The median is marked by a line within the box
 Whiskers: two lines outside the box extend to
Minimum and Maximum

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Histogram Analysis
 Graph displays of basic statistical class descriptions
 Frequency histograms
 A univariate graphical method
 Consists of a set of rectangles that reflect the counts or
frequencies of the classes present in the given data

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Quantile Plot
 Displays all of the data (allowing the user to assess both
the overall behavior and unusual occurrences)
 Plots quantile information
 For a data xi data sorted in increasing order, fi
indicates that approximately 100 fi% of the data are
below or equal to the value xi

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Quantile-Quantile (Q-Q) Plot
 Graphs the quantiles of one univariate distribution against
the corresponding quantiles of another
 Allows the user to view whether there is a shift in going
from one distribution to another

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Scatter plot
 Provides a first look at bivariate data to see clusters of
points, outliers, etc
 Each pair of values is treated as a pair of coordinates and
plotted as points in the plane

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Positively and Negatively Correlated Data

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Graphic Displays of Basic Statistical Descriptions
 Histogram: (shown before)
 Boxplot: (covered before)
 Quantile plot: each value xi is paired with fi indicating
that approximately 100 fi % of data are  xi
 Quantile-quantile (q-q) plot: graphs the quantiles of one
univariant distribution against the corresponding quantiles
of another
 Scatter plot: each pair of values is a pair of coordinates
and plotted as points in the plane

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 Data cleaning
 Data reduction
 Summary

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Data Cleaning
 Importance
 “Data cleaning is one of the three biggest problems
in data warehousing”—Ralph Kimball
 “Data cleaning is the number one problem in data
warehousing”—DCI survey
 Data cleaning tasks
 Fill in missing values
 Identify outliers and smooth out noisy data
 Correct inconsistent data
 Resolve redundancy caused by data integration

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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.

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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?
 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 Bayesian
formula or decision tree

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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

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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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Simple Discretization Methods: 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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Regression
x
y
y = x + 1
X1
Y1
Y1’

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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 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
 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

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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
 Careful integration of the data from multiple sources may
help reduce/avoid redundancies and inconsistencies and
improve mining speed and quality

38
Correlation Analysis (Numerical 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(AB) 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; rA,B < 0: negatively correlated
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Correlation Analysis (Categorical Data)
 Χ2 (chi-square) test
 The larger the Χ2 value, the more likely the variables are
related
 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
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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

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Data Transformation: 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
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 Data cleaning
 Data reduction
 Summary

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Data Reduction Strategies
 Why data Reduction?
 A database/data 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
 Obtain a reduced representation of the data set that is much
smaller in volume, yet closely maintains the integrity of the
original data.
 Mining on small data set should be more efficient yet produce
the same (or almost the same) analytical results.

Data Reduction Strategies
 Data reduction strategies
 Data cube aggregation: aggregation operations are applied to the
data in the construction of a data cube.
 Attribute subset selection: irrelevant, weekly relevant, or
redundant attributes or dimensions may be detected and
removed.
 Dimensionality reduction : encoding mechanisms are used to
reduce the data set size.
 Numerosity reduction: data are replaced or estimated by
alternative, smaller data representations such as parametric
models or nonparametric model such as clustering, sampling, or
histogram e .g., fit data into models.
 Discretization and concept hierarchy generation: raw data values
for attributes are replaced by ranges or higher conceptual levels.
46

47
Data Cube Aggregation
 Stores multidimensional aggregated information.
 Provides fast access to precomputed, summarized data,
thereby, benefiting on-line analytical processing as well as
data mining.
 The cube created at the lowest level of abstraction is
referred to as the base cuboid.
 A cube at the highest level of abstraction is the apex
cuboid.
 Data cubes created for varying levels of abstraction are
often referred to as cuboids, so that a data cube may
instead refer to a lattice of cuboids.

49
Attribute Subset Selection
 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

50
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}

51
Dimensionality Reduction
 Data encoding or transformations are applied so as to
obtain a reduced or “compressed” representation of the
original data.
 Lossless data reduction.
 Lossy data reduction.
 Method for dimensionality reduction:
 Wavelet Transforms.
 Principal components analysis.
Original Data Compressed
Data
Original Data
Approximated
lossless

57
Numerosity Reduction
 Reduce data volume by choosing alternative, smaller
forms of data representation
 Non-parametric methods
 Major families: histograms, clustering, sampling

58
Data Reduction Method (1):
Regression and Log-Linear Models
 Linear regression: used to approximate the given data.
 Data are modeled to fit a straight line
 Often uses the least-square method to fit the line

60
Data Reduction Method (2): Histograms
 Divide data into buckets and store
average (sum) for each bucket
0
5
10
15
20
25
30
35
40
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000

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Data Reduction Method (3): 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”
 There are many choices of clustering definitions and clustering
algorithms

63
Data Reduction Method (4): 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
 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

64
Sampling: with or without Replacement
Raw Data

65
Sampling: Cluster or Stratified Sampling
Raw Data Cluster/Stratified Sample

66
 Data cleaning
 Data reduction
 Summary

67
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
 Continuous — real numbers, e.g., integer or real numbers
 Discretization:
 Reduce data size by discretization
 Prepare for further analysis

68
Discretization and Concept Hierarchy
 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
 Supervised vs. unsupervised
 Split (top-down) vs. merge (bottom-up)
 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 young, middle-aged, or senior)

69
Discretization and Concept Hierarchy
Generation for Numeric Data
 Typical methods: All the methods can be applied recursively
 Binning (covered above)
 Top-down split, unsupervised,
 Histogram analysis (covered above)
 Top-down split, unsupervised
 Clustering analysis (covered above)
 Either top-down split or bottom-up merge, unsupervised
 Entropy-based discretization: supervised, top-down split
 Interval merging by 2 Analysis: unsupervised, bottom-up merge
 Segmentation by natural partitioning: top-down split, unsupervised

70
Entropy-Based Discretization
 Given a set of samples S, if S is partitioned into two intervals S1 and S2
using boundary T, the information gain after partitioning is
 Entropy is calculated based on class distribution of the samples in the
set. Given m classes, the entropy of S1 is
where pi is the probability of class i in S1
 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
 Such a boundary may reduce data size and improve classification
accuracy
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71
Interval Merge by 2 Analysis
 Merging-based (bottom-up) vs. splitting-based methods
 Merge: Find the best neighboring intervals and merge them to form
larger intervals recursively
 ChiMerge [Kerber AAAI 1992, See also Liu et al. DMKD 2002]
 Initially, each distinct value of a numerical attr. A is considered to be
one interval
 2 tests are performed for every pair of adjacent intervals
 Adjacent intervals with the least 2 values are merged together,
since low 2 values for a pair indicate similar class distributions
 This merge process proceeds recursively until a predefined stopping
criterion is met (such as significance level, max-interval, max
inconsistency, etc.)

72
Segmentation by Natural Partitioning
 Above Discretization are useful in the generation of
numerical hierarchies.
 Many users would like to see numerical ranges
partitioned into relatively uniform, easy-to-read intervals
that appear “natural”.
 For ex. Annual salaries broken into ranges like ($50,000-
$60,000] are often more desirable then ranges like
($51,263.98 - $60,872.34].

Methods
1. Specification of a partial ordering of attributes
explicitly at the schema level by users or
experts.
 typically involve a group of attributes.
 A user or expert can easily define a concept hierarchy by
specifying a partial or total ordering of the attributes at the
schema level.
 For ex dimension “location” contains the following group of
attributes : street, city, province or state, and country.
 A hierarchy can be defined by specifying the total ordering
among these attributes at the schema level,
Such as street < city < province or state < country
74

Methods
2. Specification of a portion of a hierarchy by
explicit data grouping:
 This is essentially the manual definition of a portion of a
concept hierarchy.
 For large database it is unrealistic to define an entire
concept hierarchy by explicit value enumeration.
 For ex. On the contrary, we can easily specify explicit
groupings for a small portion of intermediate-level data.
 User could define a some intermediate levels manually,
such as
“{Alberta, Saskatchewan, Manitobag } ɛ prairies Canada” and
“{British Columbia, prairies Canadag} ɛ Western Canada”.
75

Methods
3. Specification of a set of attributes, but not of their
partial ordering:
 The system can try to automatically generate the attribute ordering
so as to construct a meaningful concept hierarchy based on
following observation.
 Observation : An attribute defining a high concept level (country)
will usully contain a smaller number of distinct values than an
attribute defining a lower concept level (street).
 Based on this observation, a concept hierarcy can be automatically
generated based on 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.
 The lower the number of distinct values an attribute has, the higher
it is in the generated concept hierarchy.
76

Methods
 Example: A concept hierarchy for location can be
generated automatically. As shown in fig
 First, sort the attributes in ascending order based on the
number of distinct values.
 Second, generate the hierarchy from the top down
according to the sorted order, with the first attribute at
the top level and the
last attribute at the
bottom level.
77
country
province_or_ state
city
street
15 distinct values
365 distinct
values
3567 distinct values
674,339 distinct values

78
 Data cleaning
 Data reduction
 Discretization and concept hierarchy
generation
 Summary

79
Summary
 Data preparation or preprocessing is a big issue for both
data warehousing and data mining
 Discriptive data summarization is need for quality data
preprocessing
 Data preparation includes
 Data cleaning and data integration
 Data reduction and feature selection
 Discretization
 A lot a methods have been developed but data
preprocessing still an active area of research

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