Data Mining: Concepts and Techniques — Chapter 2 —

1
Data Mining:
Concepts and Techniques
— Chapter 2 —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign
Simon Fraser University
©2013 Han, Kamber, and Pei. All rights reserved.

September 14, 2014 Data Mining: Concepts and Techniques 2

3
Chapter 2: Getting to Know Your Data
 Data Objects and Attribute Types
 Basic Statistical Descriptions of Data
 Data Visualization
 Measuring Data Similarity and Dissimilarity
 Summary

4
Types of Data Sets
 Record
 Relational records
 Data matrix, e.g., numerical matrix,
crosstabs
 Document data: text documents: term-frequency
vector
 Transaction data
 Graph and network
 World Wide Web
 Social or information networks
 Molecular Structures
 Ordered
 Video data: sequence of images
 Temporal data: time-series
 Sequential Data: transaction sequences
 Genetic sequence data
 Spatial, image and multimedia:
 Spatial data: maps
 Image data:
 Video data:
Document 1
season
timeout
lost
wi
n
game
score
ball
pla
y
coach
team
Document 2
Document 3
3 0 5 0 2 6 0 2 0 2
0
0
7 0 2 1 0 0 3 0 0
1 0 0 1 2 2 0 3 0
TID Items
1 Bread, Coke, Milk
2 Beer, Bread
3 Beer, Coke, Diaper, Milk
4 Beer, Bread, Diaper, Milk
5 Coke, Diaper, Milk

5
Important Characteristics of Structured
Data
 Dimensionality
 Curse of dimensionality
 Sparsity
 Only presence counts
 Resolution
 Patterns depend on the scale
 Distribution
 Centrality and dispersion

6
Data Objects
 Data sets are made up of data objects.
 A data object represents an entity.
 Examples:
 sales database: customers, store items, sales
 medical database: patients, treatments
 university database: students, professors, courses
 Also called samples , examples, instances, data points, objects,
tuples.
 Data objects are described by attributes.
 Database rows -> data objects; columns ->attributes.

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Attributes
 Attribute (or dimensions, features, variables): a data
field, representing a characteristic or feature of a data
object.
 E.g., customer _ID, name, address
 Types:
 Nominal
 Binary
 Numeric: quantitative
 Interval-scaled
 Ratio-scaled

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Attribute Types
 Nominal: categories, states, or “names of things”
 Hair_color = {auburn, black, blond, brown, grey, red, white}
 marital status, occupation, ID numbers, zip codes
 Binary
 Nominal attribute with only 2 states (0 and 1)
 Symmetric binary: both outcomes equally important
 e.g., gender
 Asymmetric binary: outcomes not equally important.
 e.g., medical test (positive vs. negative)
 Convention: assign 1 to most important outcome (e.g., HIV
positive)
 Ordinal
 Values have a meaningful order (ranking) but magnitude between
successive values is not known.
 Size = {small, medium, large}, grades, army rankings

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Numeric Attribute Types
 Quantity (integer or real-valued)
 Interval
 Measured on a scale of equal-sized units
 Values have order
 E.g., temperature in C˚or F˚, calendar dates
 No true zero-point
 Ratio
 Inherent zero-point
 We can speak of values as being an order of magnitude
larger than the unit of measurement (10 K˚ is twice as
high as 5 K˚).
 e.g., temperature in Kelvin, length, counts,
monetary quantities

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Discrete vs. Continuous Attributes
 Discrete Attribute
 Has only a finite or countably infinite set of values
 E.g., zip codes, profession, or the set of words in a
collection of documents
 Sometimes, represented as integer variables
 Note: Binary attributes are a special case of discrete
attributes
 Continuous Attribute
 Has real numbers as attribute values
 E.g., temperature, height, or weight
 Practically, real values can only be measured and
represented using a finite number of digits
 Continuous attributes are typically represented as floating-point
variables

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 Summary

12
Basic Statistical Descriptions of Data
 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

x   
13
Measuring the Central Tendency
 Mean (algebraic measure) (sample vs. population):
Note: n is sample size and N is population size.
 Weighted arithmetic mean:
 Trimmed mean: chopping extreme values
 Median:
  n
 Middle value if odd number of values, or average of the
middle two values otherwise
 Estimated by interpolation (for grouped data):
 Mode
n freq
 Value that occurs most frequently in the data
 Unimodal, bimodal, trimodal
 Empirical formula:



n
i
i x
n
x
1
1

w x


i
i
n
i
i i
w
x
1
1
width
freq
median L
median
l )
/ 2 ( )
( 1
 
 
meanmode  3(meanmedian)
N
Median
interval

Symmetric vs. Skewed
Data
 Median, mean and mode of
symmetric, positively and negatively
skewed data
symmetric
positively skewed negatively skewed

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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, median,Q3, max
 Boxplot: ends of the box are the quartiles; median is marked; add whiskers,
and plot outliers individually
 Outlier: usually, a value higher/lower than 1.5 x IQR
 Variance and standard deviation (sample: s, population: σ)
 Variance: (algebraic, scalable computation)
2 2
2 2 ( ) ]
  
 


  

i i
2 2 1
 Standard deviation s (or σ) is the square root of variance s2 (or σ2)

n
i
n
i
n
i
i x
n
x
n
x x
n
s
1 1
1
1
[
1
1
( )
1
1
n
 
 
   
i
i
n
i
i x
N
x
N 1
2 2
1
( )
1
  

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Boxplot Analysis
 Five-number summary of a distribution
 Minimum, Q1, Median, 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 IQR
 The median is marked by a line within the box
 Whiskers: two lines outside the box extended
to Minimum and Maximum
 Outliers: points beyond a specified outlier
threshold, plotted individually

Visualization of Data Dispersion: 3-D Boxplots

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Properties of Normal Distribution Curve
 The normal (distribution) curve
 From μ–σ to μ+σ: contains about 68% of the measurements
(μ: mean, σ: standard deviation)
 From μ–2σ to μ+2σ: contains about 95% of it
 From μ–3σ to μ+3σ: contains about 99.7% of it

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Graphic Displays of Basic Statistical
Descriptions
 Boxplot: graphic display of five-number summary
 Histogram: x-axis are values, y-axis repres. frequencies
 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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Histogram Analysis
 Histogram: Graph display of tabulated
frequencies, shown as bars
40
35
 It shows what proportion of cases fall
into each of several categories
 Differs from a bar chart in that it is
the area of the bar that denotes the
value, not the height as in bar charts,
a crucial distinction when the
categories are not of uniform width
30
25
20
15
10
5
 The categories are usually specified as
non-overlapping intervals of some
variable. The categories (bars) must
be adjacent
0
10000 30000 50000 70000 90000

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Histograms Often Tell More than Boxplots
 The two histograms
shown in the left may
have the same boxplot
representation
 The same values for:
min, Q1, median, Q3,
max
 But they have rather
different data
distributions

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
Data Mining: Concepts and Techniques 22

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Quantile-Quantile (Q-Q) Plot
 Graphs the quantiles of one univariate distribution against the
corresponding quantiles of another
 View: Is there is a shift in going from one distribution to another?
 Example shows unit price of items sold at Branch 1 vs. Branch 2 for
each quantile. Unit prices of items sold at Branch 1 tend to be lower
than those at Branch 2.

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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
 The left half fragment is positively
correlated
 The right half is negative correlated

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 Summary

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Data Visualization
 Why data visualization?
 Gain insight into an information space by mapping data onto graphical
primitives
 Provide qualitative overview of large data sets
 Search for patterns, trends, structure, irregularities, relationships among
data
 Help find interesting regions and suitable parameters for further
quantitative analysis
 Provide a visual proof of computer representations derived
 Categorization of visualization methods:
 Pixel-oriented visualization techniques
 Geometric projection visualization techniques
 Icon-based visualization techniques
 Hierarchical visualization techniques
 Visualizing complex data and relations

Pixel-Oriented Visualization Techniques
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 For a data set of m dimensions, create m windows on the screen, one
for each dimension
 The m dimension values of a record are mapped to m pixels at the
corresponding positions in the windows
 The colors of the pixels reflect the corresponding values
(a) Income (b) Credit Limit (c) transaction volume (d) age

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Laying Out Pixels in Circle Segments
 To save space and show the connections among multiple dimensions,
space filling is often done in a circle segment
(a) Representing a data record
in circle segment
(b) Laying out pixels in circle segment
Representing about 265,000 50-dimensional Data Items
with the ‘Circle Segments’ Technique

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Geometric Projection Visualization
Techniques
 Visualization of geometric transformations and projections of
the data
 Methods
 Direct visualization
 Scatterplot and scatterplot matrices
 Landscapes
 Projection pursuit technique: Help users find meaningful
projections of multidimensional data
 Prosection views
 Hyperslice
 Parallel coordinates

Direct Data Visualization
Ribbons with Twists Based on Vorticity

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Scatterplot Matrices
Used by ermission of M. Ward, Worcester Polytechnic Institute
Matrix of scatterplots (x-y-diagrams) of the k-dim. data [total of (k2/2-k) scatterplots]

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news articles
visualized as
a landscape
Used by permission of B. Wright, Visible Decisions Inc.
Landscapes
 Visualization of the data as perspective landscape
 The data needs to be transformed into a (possibly artificial) 2D spatial
representation which preserves the characteristics of the data

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Parallel Coordinates
 n equidistant axes which are parallel to one of the screen axes and
 The axes are scaled to the [minimum, maximum]: range of the
 Every data item corresponds to a polygonal line which intersects each of the
axes at the point which corresponds to the value for the attribute
• • •
correspond to the attributes
corresponding attribute
Attr. 1 Attr . 2 Attr. 3 Attr. k

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Parallel Coordinates of a Data Set

37
Icon-Based Visualization Techniques
 Visualization of the data values as features of icons
 Typical visualization methods
 Chernoff Faces
 Stick Figures
 General techniques
 Shape coding: Use shape to represent certain information
encoding
 Color icons: Use color icons to encode more information
 Tile bars: Use small icons to represent the relevant feature
vectors in document retrieval

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Chernoff Faces
 A way to display variables on a two-dimensional surface, e.g., let x be
eyebrow slant, y be eye size, z be nose length, etc.
 The figure shows faces produced using 10 characteristics--head eccentricity,
eye size, eye spacing, eye eccentricity, pupil size, eyebrow slant, nose size,
mouth shape, mouth size, and mouth opening): Each assigned one of 10
possible values, generated using Mathematica (S. Dickson)
 REFERENCE: Gonick, L. and Smith, W. The
Cartoon Guide to Statistics. New York: Harper
Perennial, p. 212, 1993
 Weisstein, Eric W. "Chernoff Face." From
MathWorld--A Wolfram Web Resource.
mathworld.wolfram.com/ChernoffFace.html

A census data
figure showing
age, income,
gender,
education, etc.
Stick Figure
A 5-piece stick
figure (1 body
and 4 limbs w.
different
angle/length)

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Hierarchical Visualization Techniques
 Visualization of the data using a hierarchical
partitioning into subspaces
 Methods
 Dimensional Stacking
 Worlds-within-Worlds
 Tree-Map
 Cone Trees
 InfoCube

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Dimensional Stacking
 Partitioning of the n-dimensional attribute space in 2-D
subspaces, which are ‘stacked’ into each other
 Partitioning of the attribute value ranges into classes. The
important attributes should be used on the outer levels.
 Adequate for data with ordinal attributes of low cardinality
 But, difficult to display more than nine dimensions
 Important to map dimensions appropriately

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Dimensional Stacking
Used by permission of M. Ward, Worcester Polytechnic Institute
Visualization of oil mining data with longitude and latitude mapped to the
outer x-, y-axes and ore grade and depth mapped to the inner x-, y-axes

43
Worlds-within-Worlds
 Assign the function and two most important parameters to innermost
world
 Fix all other parameters at constant values - draw other (1 or 2 or 3
dimensional worlds choosing these as the axes)
 Software that uses this paradigm
 N–vision: Dynamic
interaction through data
glove and stereo displays,
including rotation, scaling
(inner) and translation
(inner/outer)
 Auto Visual: Static
interaction by means of
queries

44
Tree-Map
 Screen-filling method which uses a hierarchical partitioning of
the screen into regions depending on the attribute values
 The x- and y-dimension of the screen are partitioned alternately
according to the attribute values (classes)
Schneiderman@UMD: Tree-Map of a File System Schneiderman@UMD: Tree-Map to support
large data sets of a million items

45
InfoCube
 A 3-D visualization technique where hierarchical
information is displayed as nested semi-transparent
cubes
 The outermost cubes correspond to the top level data,
while the subnodes or the lower level data are
represented as smaller cubes inside the outermost
cubes, and so on

46
Three-D Cone Trees
 3D cone tree visualization technique works
well for up to a thousand nodes or so
 First build a 2D circle tree that arranges its
nodes in concentric circles centered on the
root node
 Cannot avoid overlaps when projected to 2D
 G. Robertson, J. Mackinlay, S. Card. “Cone
Trees: Animated 3D Visualizations of
Hierarchical Information”, ACM SIGCHI'91
 Graph from Nadeau Software Consulting
website: Visualize a social network data set
that models the way an infection spreads from
one person to the next

Visualizing Complex Data and Relations
 Visualizing non-numerical data: text and social networks
 Tag cloud: visualizing user-generated tags
 The importance of tag is
represented by font
size/color
 Besides text data, there are
also methods to visualize
relationships, such as
visualizing social networks
Newsmap: Google News Stories in 2005

48
 Summary

49
Similarity and Dissimilarity
 Similarity
 Numerical measure of how alike two data objects are
 Value is higher when objects are more alike
 Often falls in the range [0,1]
 Dissimilarity (e.g., distance)
 Numerical measure of how different two data objects are
 Lower when objects are more alike
 Minimum dissimilarity is often 0
 Upper limit varies
 Proximity refers to a similarity or dissimilarity

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Data Matrix and Dissimilarity Matrix
 Data matrix
 n data points with p
dimensions
 Two modes
 Dissimilarity matrix
 n data points, but
registers only the
distance
 A triangular matrix
 Single mode


















x11 ... x1f ... x1p
... ... ... ... ...
xi1 ... xif ... xip
... ... ... ... ...
np
... x
nf
... x
n1
x
















0
d(2,1) 0
d(3,1 ) d (3,2)
0
: : :
d n d n ...
( ,1) ( ,2) ... 0

51
Proximity Measure for Nominal Attributes
 Can take 2 or more states, e.g., red, yellow, blue,
green (generalization of a binary attribute)
 Method 1: Simple matching
 m: # of matches, p: total # of variables
p 
m
p
d i j
( , )
 Method 2: Use a large number of binary attributes
 creating a new binary attribute for each of the M
nominal states

52
Proximity Measure for Binary Attributes
 A contingency table for binary data
 Distance measure for symmetric
binary variables:
 Distance measure for asymmetric
binary variables:
 Jaccard coefficient (similarity
measure for asymmetric binary
variables):
Object i
 Note: Jaccard coefficient is the same as “coherence”:
Object j

53
Dissimilarity between Binary Variables
 Example
Name Gender Fever Cough Test-1 Test-2 Test-3 Test-4
Jack M Y N P N N N
Mary F Y N P N P N
Jim M Y P N N N N
 Gender is a symmetric attribute
 The remaining attributes are asymmetric binary
 Let the values Y and P be 1, and the value N 0
0.75

0 1

1 1

1 2
1 1 2
d jack mary
d jack jim
( , )
0.67
1 1 1
( , )
0.33
2 0 1
( , )

 


 


 

d jim mary

54
Standardizing Numeric Data
 Z-score:

 X: raw score to be standardized, μ: mean of the population, σ: standard
deviation
 the distance between the raw score and the population mean in units
of the standard deviation
 negative when the raw score is below the mean, “+” when above
 An alternative way: Calculate the mean absolute deviation
where
1(| | | | ... | |)
f 1f f 2 f f nf f s  n x m  x m   x m
 standardized measure (z-score):
x 
m
if f
z

 Using mean absolute deviation is more robust than using standard
deviation
... ).
1 2
1
f f f nf m  n(x  x   x
f
if s


x
z

55
Example:
Data Matrix and Dissimilarity Matrix
Data Matrix
point attribute1 attribute2
x1 1 2
x2 3 5
x3 2 0
x4 4 5
Dissimilarity Matrix
(with Euclidean Distance)
x1 x2 x3 x4
x1 0
x2 3.61 0
x3 2.24 5.1 0
x4 4.24 1 5.39 0

56
Distance on Numeric Data: Minkowski
Distance
 Minkowski distance: A popular distance measure
where i = (xi1, xi2, …, xip) and j = (xj1, xj2, …, xjp) are two p-dimensional
data objects, and h is the order (the distance
so defined is also called L-h norm)
 Properties
 d(i, j) > 0 if i ≠ j, and d(i, i) = 0 (Positive definiteness)
 d(i, j) = d(j, i) (Symmetry)
 d(i, j)  d(i, k) + d(k, j) (Triangle Inequality)
 A distance that satisfies these properties is a metric

57
Special Cases of Minkowski Distance
 h = 1: Manhattan (city block, L1 norm) distance
 E.g., the Hamming distance: the number of bits that are different
between two binary vectors
x
d i j  x   x
   x

( , ) | | | | ... | |
 h = 2: (L2 norm) Euclidean distance
( , ) (| | | | ... | | ) 2 2
d i j  x   x
   x

 h  . “supremum” (Lmax norm, L norm) distance.
x
 This is the maximum difference between any component (attribute)
of the vectors
2 2
2
1 1 j
i
p jp
x
i
j
x
i
1 1 2 j
2 i
p jp
x
i
j
x
i

58
Example: Minkowski Distance
Dissimilarity Matrices
point attribute 1 attribute 2
x1 1 2
x2 3 5
x3 2 0
x4 4 5
Manhattan (L1)
L x1 x2 x3 x4
x1 0
x2 5 0
x3 3 6 0
x4 6 1 7 0
Euclidean (L2)
L2 x1 x2 x3 x4
x1 0
x2 3.61 0
x3 2.24 5.1 0
x4 4.24 1 5.39 0
Supremum
L x1 x2 x3 x4
x1 0
x2 3 0
x3 2 5 0
x4 3 1 5 0

59
Ordinal Variables
 An ordinal variable can be discrete or continuous
 Order is important, e.g., rank
 Can be treated like interval-scaled
 replace xif by their rank
{1,..., } if f r  M
 map the range of each variable onto [0, 1] by replacing i-th
object in the f-th variable by

1

1

f
r
if
z
if M
 compute the dissimilarity using methods for interval-scaled
variables

60
Attributes of Mixed Type
 A database may contain all attribute types
 Nominal, symmetric binary, asymmetric binary, numeric,
ordinal
 One may use a weighted formula to combine their effects
 f is binary or nominal:
dij
p
f d
(f) = 0 if xif = xjf , or dij
( ) ( )

(f) = 1 otherwise
 f is numeric: use the normalized distance
 f is ordinal
 Compute ranks rif and
 Treat zif as interval-scaled
( )
1
1 ( , )
f
ij
p
f
f
ij
f
ij
d i j






1

1


f
r
if
M
zif

61
Cosine Similarity
 A document can be represented by thousands of attributes, each recording the
frequency of a particular word (such as keywords) or phrase in the document.
 Other vector objects: gene features in micro-arrays, …
 Applications: information retrieval, biologic taxonomy, gene feature mapping,
...
 Cosine measure: If d1 and d2 are two vectors (e.g., term-frequency vectors),
then
cos(d1, d2) = (d1  d2) /||d1|| ||d2|| ,
where  indicates vector dot product, ||d||: the length of vector d

62
Example: Cosine Similarity
 cos(d1, d2) = (d1  d2) /||d1|| ||d2|| ,
where  indicates vector dot product, ||d|: the length of vector d
 Ex: Find the similarity between documents 1 and 2.
d1 = (5, 0, 3, 0, 2, 0, 0, 2, 0, 0)
d2 = (3, 0, 2, 0, 1, 1, 0, 1, 0, 1)
d1d2 = 5*3+0*0+3*2+0*0+2*1+0*1+0*1+2*1+0*0+0*1 = 25
||d1||= (5*5+0*0+3*3+0*0+2*2+0*0+0*0+2*2+0*0+0*0)0.5=(42)0.5 = 6.481
||d2||= (3*3+0*0+2*2+0*0+1*1+1*1+0*0+1*1+0*0+1*1)0.5=(17)0.5 = 4.12
cos(d1, d2 ) = 0.94

63
KL Divergence: Comparing Two
Probability Distributions
 The Kullback-Leibler (KL) divergence: Measure the difference between two
probability distributions over the same variable x
 From information theory, closely related to relative entropy, information
divergence, and information for discrimination
 DKL(p(x) || q(x)): divergence of q(x) from p(x), measuring the information lost
when q(x) is used to approximate p(x)
 Discrete form:
 The KL divergence measures the expected number of extra bits required to
code samples from p(x) (“true” distribution) when using a code based on q(x),
which represents a theory, model, description, or approximation of p(x)
 Its continuous form:
 The KL divergence: not a distance measure, not a metric: asymmetric, not
satisfy triangular inequality

64
How to Compute
the KL Divergence?
 Base on the formula, DKL(P,Q) ≥ 0 and DKL(P || Q) = 0 if and only if P = Q.
 How about when p = 0 or q = 0?
 limp→0 p log p = 0
 when p != 0 but q = 0, DKL(p || q) is defined as ∞, i.e., if one event e is
possible (i.e., p(e) > 0), and the other predicts it is absolutely impossible
(i.e., q(e) = 0), then the two distributions are absolutely different
 However, in practice, P and Q are derived from frequency distributions, not
counting the possibility of unseen events. Thus smoothing is needed
 Example: P : (a : 3/5, b : 1/5, c : 1/5). Q : (a : 5/9, b : 3/9, d : 1/9)
 need to introduce a small constant ϵ, e.g., ϵ = 10−3
 The sample set observed in P, SP = {a, b, c}, SQ = {a, b, d}, SU = {a, b, c, d}
 Smoothing, add missing symbols to each distribution, with probability ϵ
 P′ : (a : 3/5 − ϵ/3, b : 1/5 − ϵ/3, c : 1/5 − ϵ/3, d : ϵ)
 Q′ : (a : 5/9 − ϵ/3, b : 3/9 − ϵ/3, c : ϵ, d : 1/9 − ϵ/3).
 DKL(P’ || Q’) can be computed easily

65
 Summary

Summary
 Data attribute types: nominal, binary, ordinal, interval-scaled,
ratio-scaled
 Many types of data sets, e.g., numerical, text, graph, Web,
image.
 Gain insight into the data by:
 Basic statistical data description: central tendency,
dispersion, graphical displays
 Data visualization: map data onto graphical primitives
 Measure data similarity
 Above steps are the beginning of data preprocessing
 Many methods have been developed but still an active area of
research

References
 W. Cleveland, Visualizing Data, Hobart Press, 1993
 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley, 2003
 U. Fayyad, G. Grinstein, and A. Wierse. Information Visualization in Data Mining and
Knowledge Discovery, Morgan Kaufmann, 2001
 L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster
Analysis. John Wiley & Sons, 1990.
 H. V. Jagadish et al., Special Issue on Data Reduction Techniques. Bulletin of the Tech.
Committee on Data Eng., 20(4), Dec. 1997
 D. A. Keim. Information visualization and visual data mining, IEEE trans. on
Visualization and Computer Graphics, 8(1), 2002
 D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999
 S. Santini and R. Jain,” Similarity measures”, IEEE Trans. on Pattern Analysis and
Machine Intelligence, 21(9), 1999
 E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001
 C. Yu et al., Visual data mining of multimedia data for social and behavioral studies,
Information Visualization, 8(1), 2009

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Data Mining: Concepts and Techniques — Chapter 2 —