Transcript Data Mining: Concepts and Techniques

Data Mining:
Concepts and Techniques
— Chapter 2 —
TUGAS 1 dikiumpulkan tanggal 10 April 2010 ( PRogramming ) 2orang 1
kelompok
April 13, 2017
Data Mining: Concepts and Techniques
1
Chapter 2: Data Preprocessing

Karakteristik data secara umum

Diskripsi data dan eksplorasi

Mengukur kesamaan data

Data cleaning

Integrasi data dan transformasi

Reduksi data

Kesimpulan
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Types of Attribute Values





Nominal
 E.g., profession, ID numbers, eye color, zip codes
Ordinal
 E.g., rankings (e.g., army, professions), grades, height
in {tall, medium, short}
Binary
 E.g., medical test (positive vs. negative)
Interval
 E.g., calendar dates, body temperatures
Ratio

E.g., temperature in Kelvin, length, time, counts
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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
 Examples: 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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Chapter 2: Data Preprocessing

General data characteristics

Basic data description and exploration

Measuring data similarity

Data cleaning

Data integration and transformation

Data reduction

Summary
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Mining Data Descriptive Characteristics

Motivasi


Karakteristik dari sebaran data


Untuk memahami data: sebaran, kecenderungan
terpusat, dan variasi
median, max, min, quartiles, outliers, variance
Dimensi numerik terkait dengan interval yang terurut

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Boxplot atau quantile analysis pada interval yang
terurut
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Mengukur kecenderungan terpusat ( Central
Tendency)

Rata-rata (sample vs. population):



1 n
x   xi
n i 1
Weighted arithmetic mean:
x
N
n
Trimmed mean: chopping extreme values
x
Median: A holistic measure


w x
i 1
n
i
i
w
i 1
i
Middle value if odd number of values, or average of the middle two
values otherwise


Estimated by interpolation (for grouped data):
median  L1  (
Mode

Value that occurs most frequently in the data

Unimodal, bimodal, trimodal

Empirical formula:
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N / 2  ( freq)l
freqmedian
) width
mean  mode  3  (mean  median)
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Symmetric vs. Skewed Data

Median, mean and mode of
symmetric, positively and
negatively skewed data
positively skewed
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symmetric
negatively skewed
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Contoh : Upah Karyawan PT. Satria Semarang
Upah Harian
F
200 - 219
220 - 239
240 - 259
260 - 279
280 - 299
300 - 319
320 - 339
4
8
17
24
15
9
5
F.Kumulatif
4
12
29
53
68
77
82
F = 82
Me = 82 : 2= 41
Kelas : 260 - 279
82
259  260
TepiKelasBawah 
 259,5
2
279  280
TepiKelasA tas 
 279,5
2
F .sk
Me  TKB 
xi
Fd
12
Me  259,5 
x 20
24
240
Me  259,5 
24
Me  259,5  10
Me  269,50
F .sl
Me  TKA 
xi
Fd
12
Me  279,5  x 20
24
240
Me  279,5 
24
Me  279,5  10
 269,50
F .sk
Me  TKB 
xi
Fd
14
Me  64,5 
x10
23
140
Me  64,5 
23
Me  64,5  6,1
Me  76
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, whiskers, and
plot outlier individually


Outlier: usually, a value higher/lower than 1.5 x IQR
Variance and standard deviation (sample: s, population: σ)

Variance: (algebraic, scalable computation)
1 n
1 n 2 1 n
2
s 
( xi  x ) 
[ xi  ( xi ) 2 ]

n  1 i 1
n  1 i 1
n i 1
2

1
 
N
2
n
1
(
x


)


i
N
i 1
2
n
 xi   2
2
i 1
Standard deviation s (or σ) is the square root of variance s2 (or σ2)
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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
 Scatter plot: each pair of values is a pair of
coordinates and plotted as points in the plane
 Loess (local regression) curve: add a smooth
curve to a scatter plot to provide better
perception of the pattern of dependence

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Histogram Analysis

Graph displays of basic statistical class descriptions
 Frequency histograms


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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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Histograms Often Tells More than Boxplots

The two histograms
shown in the left may
have the same boxplot
representation


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The same values
for: min, Q1,
median, Q3, max
But they have rather
different data
distributions
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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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Loess Curve


Adds a smooth curve to a scatter plot in order to
provide better perception of the pattern of dependence
Loess curve is fitted by setting two parameters: a
smoothing parameter, and the degree of the
polynomials that are fitted by the regression
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Positively and Negatively Correlated Data

The left half fragment is positively
correlated

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The right half is negative correlated
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Not Correlated Data
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Used by permission of M. Ward, Worcester Polytechnic Institute
Scatterplot Matrices
Matrix of scatterplots (x-y-diagrams) of the k-dim. data [total of C(k, 2) = (k2 ̶ k)/2 scatterplots]
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Chapter 2: Data Preprocessing

General data characteristics

Basic data description and exploration

Measuring data similarity (Sec. 7.2)

Data cleaning

Data integration and transformation

Data reduction

Summary
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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 (i.e., distance)
 Numerical measure of how different are two data
objects
 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
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 x11

 ...
x
 i1
 ...
x
 n1
...
x1f
...
...
...
...
xif
...
...
...
...
... xnf
...
...
 0
 d(2,1)
0

 d(3,1) d ( 3,2) 0

:
:
 :
d ( n,1) d ( n,2) ...
Data Mining: Concepts and Techniques
x1p 

... 
xip 

... 
xnp 







... 0
25
Example: Data Matrix and Distance Matrix
3
point
p1
p2
p3
p4
p1
2
p3
p4
1
p2
0
0
1
2
3
4
5
p1
p2
p3
p4
0
2.828
3.162
5.099
y
2
0
1
1
Data Matrix
6
p1
x
0
2
3
5
p2
2.828
0
1.414
3.162
p3
3.162
1.414
0
2
p4
5.099
3.162
2
0
Distance Matrix (i.e., Dissimilarity Matrix) for Euclidean Distance
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Minkowski Distance

Minkowski distance: A popular distance measure
d (i, j)  q (| x  x |q  | x  x |q ... | x  x |q )
i1 j1
i2
j2
ip
jp
where i = (xi1, xi2, …, xip) and j = (xj1, xj2, …, xjp) are two
p-dimensional data objects, and q is the order


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
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Special Cases of Minkowski Distance

q = 1: Manhattan (city block, L1 norm) distance

E.g., the Hamming distance: the number of bits that are
different between two binary vectors
d (i, j) | x  x |  | x  x | ... | x  x |
i1 j1
i2 j 2
ip
jp

q= 2: (L2 norm) Euclidean distance
d (i, j)  (| x  x |2  | x  x |2 ... | x  x |2 )
i1 j1
i2
j2
ip
jp

q  . “supremum” (Lmax norm, L norm) distance.
This is the maximum difference between any component of the
vectors
Do not confuse q with n, i.e., all these distances are defined for all
numbers of dimensions.
Also, one can use weighted distance, parametric Pearson product
moment correlation, or other dissimilarity measures



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Example: Minkowski Distance
point
p1
p2
p3
p4
x
0
2
3
5
y
2
0
1
1
L1
p1
p2
p3
p4
p1
0
4
4
6
p2
4
0
2
4
p3
4
2
0
2
p4
6
4
2
0
L2
p1
p2
p3
p4
p1
p2
2.828
0
1.414
3.162
p3
3.162
1.414
0
2
p4
5.099
3.162
2
0
L
p1
p2
p3
p4
p1
p2
p3
p4
0
2.828
3.162
5.099
0
2
3
5
2
0
1
3
3
1
0
2
5
3
2
0
Distance Matrix
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Interval-valued variables

Standardize data

Calculate the mean absolute deviation:
sf  1
n (| x1 f  m f |  | x2 f  m f | ... | xnf  m f |)
where


m f  1n (x1 f  x2 f
 ... 
xnf )
.
Calculate the standardized measurement (z-score)
xif  m f
zif 
sf
Using mean absolute deviation is more robust than using standard
deviation

Then calculate the Enclidean distance of other Minkowski distance
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Binary Variables


1
0
a
b
A contingency table for binary data Object i 1
0
c
d
sum a  c b  d
Distance measure for symmetric
d (i, j) 
binary variables:

Distance measure for asymmetric
binary variables:

Jaccard coefficient (similarity
measure for asymmetric binary
variables):

Object j
d (i, j) 
sum
a b
cd
p
bc
a bc  d
bc
a bc
simJaccard (i, j) 
a
a b c
Note: Jaccard coefficient is the same as “coherence”:
coherence(i, j) 
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sup(i, j)
a

sup(i)  sup( j)  sup(i, j) (a  b)  (a  c)  a
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Dissimilarity between Binary Variables

Example
Name
Jack
Mary
Jim



Gender
M
F
M
Fever
Y
Y
Y
Cough
N
N
P
Test-1
P
P
N
Test-2
N
N
N
Test-3
N
P
N
Test-4
N
N
N
gender is a symmetric attribute
the remaining attributes are asymmetric binary
let the values Y and P be set to 1, and the value N be set to 0
01
 0.33
2 01
11
d ( jack , jim ) 
 0.67
111
1 2
d ( jim , mary ) 
 0.75
11 2
d ( jack , mary ) 
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Nominal Variables


A generalization of the binary variable in that it can take
more than 2 states, e.g., red, yellow, blue, green
Method 1: Simple matching

m: # of matches, p: total # of variables
m
d (i, j)  p 
p

Method 2: Use a large number of binary variables

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creating a new binary variable for each of the M
nominal states
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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
map the range of each variable onto [0, 1] by replacing
i-th object in the f-th variable by
zif

rif {1,...,M f }
rif 1

M f 1
compute the dissimilarity using methods for intervalscaled variables
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Ratio-Scaled Variables


Ratio-scaled variable: a positive measurement on a
nonlinear scale, approximately at exponential scale,
such as AeBt or Ae-Bt
Methods:


treat them like interval-scaled variables—not a good
choice! (why?—the scale can be distorted)
apply logarithmic transformation
yif = log(xif)

April 13, 2017
treat them as continuous ordinal data treat their rank
as interval-scaled
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Variables of Mixed Types


A database may contain all the six types of variables
 symmetric binary, asymmetric binary, nominal, ordinal,
interval and ratio
One may use a weighted formula to combine their effects
 pf  1 ij( f ) dij( f )
d (i, j) 
 pf  1 ij( f )



f is binary or nominal:
dij(f) = 0 if xif = xjf , or dij(f) = 1 otherwise
f is interval-based: use the normalized distance
f is ordinal or ratio-scaled
 Compute ranks rif and
r
1
zif 
M 1
 Treat zif as interval-scaled
if
f
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Vector Objects: Cosine Similarity




Vector objects: keywords in documents, gene features in micro-arrays, …
Applications: information retrieval, biologic taxonomy, ...
Cosine measure: If d1 and d2 are two vectors, then
cos(d1, d2) = (d1  d2) /||d1|| ||d2|| ,
where  indicates vector dot product, ||d||: the length of vector d
Example:
d1 = 3 2 0 5 0 0 0 2 0 0
d2 = 1 0 0 0 0 0 0 1 0 2
d1d2 = 3*1+2*0+0*0+5*0+0*0+0*0+0*0+2*1+0*0+0*2 = 5
||d1||= (3*3+2*2+0*0+5*5+0*0+0*0+0*0+2*2+0*0+0*0)0.5=(42)0.5
= 6.481
||d2|| = (1*1+0*0+0*0+0*0+0*0+0*0+0*0+1*1+0*0+2*2)0.5=(6) 0.5
= 2.245
cos( d1, d2 ) = .3150
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Chapter 2: Data Preprocessing

General data characteristics

Basic data description and exploration

Measuring data similarity

Data cleaning

Data integration and transformation

Data reduction

Summary
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Tugas Pokok dalam Pemrosesan awal data



Data cleaning
 Mengisi nilai yang hilang, memperhalus data noise,
mengidentifikasi atau menghilangkan outlier dan
memecahkan ketidak konsistenanan
Integrasi data
 Mengintegrasikan berbagai database, data cube atau
file-file
 Transformasi data Data transformation
 Normalisasi dan aggregation
Reduksi data

Mendapatkan representasi dalam volume data yung sudah terkurangi
tetapi menghasilkan hasil analitis yang sama atau serupa

Diskritisasi data : bagian dari reduksi data, bagian penting untuk data
numerik
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Data Cleaning

Data yang tidak berkualitas , hasil data mining yang tidak berkualitas!

Keputusan yang berkualitas harus didasarkan pada data yang
berkualitas



e.g., data ganda atau data yang hilang mungkin
menyebabkan ketidakbenaran atau bahkan menyesatkan
Ekstaksi data, pembersihan, dan transformasi data merupakan
tugas utama dalam data warehouse
Tugas-tugas data cleaning

Mengisi nilai-nilai yang hilang

Mengidentifikasi outliers dan memperhalus data noise

Memperbaiki ketidakkonsitenan data

Memecahkan redudansi yang disebabkan oleh integrasi data
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Data in the Real World Is Dirty



incomplete: lacking attribute values, lacking certain
attributes of interest, or containing only aggregate data
 e.g., children=“ ” (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=“03/07/1997”
 Was rating “1,2,3”, now rating “A, B, C”
 discrepancy between duplicate records
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Why Is Data Dirty?

Data yang tidak lengkap mungkin diperoleh dari



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 considerations between the time when the
data was collected and when it is analyzed.
Human/hardware/software problems
Different data sources
Duplicate records also need data cleaning
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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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Bagaimana mengatasi Missing Value (
data yang hilang )

Mengabaikan record-record: biasanya dilakukan bila label
class hilang (tidak efektif bila % dari nilai yang hilang per
atribut sangat diperhatikan

Mengisi nilai yang hilang secara manual

Mengisi secara otomatis dengan

Global konstant : e.g., “unknown”, a new class?!

Rata-rata dari atribut


Rata-rata atribut untuk seluruh sample dengan kelas
yang sama : smarter
nilai yang lebih memungkinkan: yaitu dengan
menggunakan metode Bayesian
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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
Other data problems which requires data cleaning
 duplicate records
 incomplete data
 inconsistent data
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45
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)
April 13, 2017
Data Mining: Concepts and Techniques
46
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
April 13, 2017
Data Mining: Concepts and Techniques
47
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

April 13, 2017
Data Mining: Concepts and Techniques
48
Regression
y
Y1
Y1’
y=x+1
X1
April 13, 2017
Data Mining: Concepts and Techniques
x
49
Cluster Analysis
April 13, 2017
Data Mining: Concepts and Techniques
50
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)
April 13, 2017
Data Mining: Concepts and Techniques
51
Chapter 2: Data Preprocessing

General data characteristics

Basic data description and exploration

Measuring data similarity

Data cleaning

Data integration and transformation

Data reduction

Summary
April 13, 2017
Data Mining: Concepts and Techniques
52
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
April 13, 2017
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53
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
April 13, 2017
Data Mining: Concepts and Techniques
54
Correlation Analysis (Numerical Data)

Correlation coefficient (also called Pearson’s product
moment coefficient)
rp ,q
( p  p)( q  q)  ( pq)  n p q



(n  1) p q
(n  1) p q
where n is the number of baris ( record)
, q and
are the
p
respective means of p and q, σp and σq are the respective standard
deviation of p and q, and Σ(pq) is the sum of the pq cross-product.


If rp,q > 0, p and q are positively correlated (p’s values
increase as q’s). The higher, the stronger correlation.
rp,q = 0: independent; rpq < 0: negatively correlated
April 13, 2017
Data Mining: Concepts and Techniques
55
Correlation (viewed as linear relationship)


Correlation measures the linear relationship
between objects
To compute correlation, we standardize data
objects, p and q, and then take their dot product
pk  ( pk  mean( p)) / std ( p)
qk  (qk  mean(q)) / std (q)
correlation( p, q)  p  q
April 13, 2017
Data Mining: Concepts and Techniques
56
Visually Evaluating Correlation
Scatter plots
showing the
similarity from
–1 to 1.
April 13, 2017
Data Mining: Concepts and Techniques
57
Correlation Analysis (Categorical Data)

Χ2 (chi-square) test
(Observed  Expected)
 
Expected
2
2



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
April 13, 2017
Data Mining: Concepts and Techniques
58
Chi-Square Calculation: An Example

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
Χ2 (chi-square) calculation (numbers in parenthesis are
expected counts calculated based on the data distribution
in the two categories)
(250  90) 2 (50  210) 2 (200  360) 2 (1000  840) 2
 



 507.93
90
210
360
840
2

It shows that like_science_fiction and play_chess are
correlated in the group
April 13, 2017
Data Mining: Concepts and Techniques
59
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
 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
April 13, 2017
Data Mining: Concepts and Techniques
60
Data Transformation: Normalization

Min-max normalization: to [new_minA, new_maxA]
v' 


v  minA
(new _ maxA  new _ minA)  new _ minA
maxA  minA
Ex. Let income range $12,000 to $98,000 normalized to [0.0,
73,600  12,000
1.0]. Then $73,000 is mapped to 98,000  12,000 (1.0  0)  0  0.716
Z-score normalization (μ: mean, σ: standard deviation):
v' 


v  A

A
Ex. Let μ = 54,000, σ = 16,000. Then
73,600  54,000
 1.225
16,000
Normalization by decimal scaling
v
v'  j
10
April 13, 2017
Where j is the smallest integer
such that Max(|ν’|) < 1
Data Mining: Concepts and Techniques
61
Chapter 2: Data Preprocessing

General data characteristics

Basic data description and exploration

Measuring data similarity

Data cleaning

Data integration and transformation

Data reduction

Summary
April 13, 2017
Data Mining: Concepts and Techniques
62
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 but yet produce the same (or almost the
same) analytical results
Data reduction strategies
 Dimensionality reduction — e.g., remove unimportant attributes
 Numerosity reduction (some simply call it: Data Reduction)
 Data cub aggregation
 Data compression
 Regression
 Discretization (and concept hierarchy generation)
April 13, 2017
Data Mining: Concepts and Techniques
63
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
 Principal component analysis
 Singular value decomposition
 Supervised and nonlinear techniques (e.g., feature selection)
April 13, 2017
Data Mining: Concepts and Techniques
64
Dimensionality Reduction: Principal
Component Analysis (PCA)


Find a projection that captures the largest amount of
variation in data
Find the eigenvectors of the covariance matrix, and these
eigenvectors define the new space
x2
e
x1
April 13, 2017
Data Mining: Concepts and Techniques
65
Principal Component Analysis (Steps)

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 data)
Works for numeric data only
April 13, 2017
Data Mining: Concepts and Techniques
66
Feature Subset Selection

Another way to reduce dimensionality of data

Redundant features



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 features


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
April 13, 2017
Data Mining: Concepts and Techniques
67
Heuristic Search in Feature Selection


There are 2d possible feature combinations of d features
Typical heuristic feature selection methods:
 Best single features under the feature independence
assumption: choose by significance tests
 Best step-wise feature selection:
 The best single-feature is picked first
 Then next best feature condition to the first, ...
 Step-wise feature elimination:
 Repeatedly eliminate the worst feature
 Best combined feature selection and elimination
 Optimal branch and bound:
 Use feature elimination and backtracking
April 13, 2017
Data Mining: Concepts and Techniques
68
Feature Creation


Create new attributes that can capture the important
information in a data set much more efficiently than the
original attributes
Three general methodologies
 Feature extraction
 domain-specific
 Mapping data to new space (see: data reduction)
 E.g., Fourier transformation, wavelet transformation
 Feature construction
 Combining features
 Data discretization
April 13, 2017
Data Mining: Concepts and Techniques
69
Mapping Data to a New Space


Fourier transform
Wavelet transform
Two Sine Waves
April 13, 2017
Two Sine Waves + Noise
Data Mining: Concepts and Techniques
Frequency
70
Numerosity (Data) 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)
 Example: 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
April 13, 2017
Data Mining: Concepts and Techniques
71
Parametric Data Reduction: 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 13, 2017
Data Mining: Concepts and Techniques
72
Regress Analysis and Log-Linear Models



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:
 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
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


Reference appropriate levels


Further reduce the size of data to deal with
Use the smallest representation which is enough to
solve the task
Queries regarding aggregated information should be
answered using data cube, when possible
April 13, 2017
Data Mining: Concepts and Techniques
74
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
April 13, 2017
Data Mining: Concepts and Techniques
75
Data Compression
Compressed
Data
Original Data
lossless
Original Data
Approximated
April 13, 2017
Data Mining: Concepts and Techniques
76
Data Reduction Method: Clustering





Partition data set into clusters based on similarity, and
store cluster representation (e.g., centroid and diameter)
only
Can be very effective if data is clustered but not if data
is “smeared”
Can have hierarchical clustering and be stored in multidimensional index tree structures
There are many choices of clustering definitions and
clustering algorithms
Cluster analysis will be studied in depth in Chapter 7
April 13, 2017
Data Mining: Concepts and Techniques
77
Data Reduction Method: 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 a
time)
April 13, 2017
Data Mining: Concepts and Techniques
78
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
April 13, 2017
Data Mining: Concepts and Techniques
79
Sampling: Cluster or Stratified Sampling
Raw Data
April 13, 2017
Cluster/Stratified Sample
Data Mining: Concepts and Techniques
80
Data Reduction: 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:

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
April 13, 2017
Data Mining: Concepts and Techniques
81
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)

Discretization can be performed recursively on an attribute
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)
April 13, 2017
Data Mining: Concepts and Techniques
82
Discretization and Concept Hierarchy
Generation for Numeric Data

Typical methods: All the methods can be applied recursively

Binning (covered above)


Histogram analysis (covered above)


Top-down split, unsupervised,
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
April 13, 2017
Data Mining: Concepts and Techniques
83
Discretization Using Class Labels

Entropy based approach
3 categories for both x and y
April 13, 2017
5 categories for both x and y
Data Mining: Concepts and Techniques
84
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
I (S , T ) 

| S1 |
|S |
Entropy( S1)  2 Entropy( S 2)
|S|
|S|
Entropy is calculated based on class distribution of the samples in the
set. Given m classes, the entropy of S1 is
m
Entropy( S1 )   pi log 2 ( pi )
i 1
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
April 13, 2017
Data Mining: Concepts and Techniques
85
Discretization Without Using Class Labels
Data
Equal frequency
April 13, 2017
Equal interval width
K-means
Data Mining: Concepts and Techniques
86
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.)
April 13, 2017
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87
Segmentation by Natural Partitioning

A simply 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 equiwidth 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
April 13, 2017
Data Mining: Concepts and Techniques
88
Example of 3-4-5 Rule
count
Step 1:
Step 2:
-$351
-$159
Min
Low (i.e, 5%-tile)
msd=1,000
profit
High(i.e, 95%-0 tile)
Low=-$1,000
(-$1,000 - 0)
(-$400 - 0)
(-$200 -$100)
(-$100 0)
April 13, 2017
Max
High=$2,000
($1,000 - $2,000)
(0 -$ 1,000)
(-$400 -$5,000)
Step 4:
(-$300 -$200)
$4,700
(-$1,000 - $2,000)
Step 3:
(-$400 -$300)
$1,838
($1,000 - $2, 000)
(0 - $1,000)
(0 $200)
($1,000 $1,200)
($200 $400)
($1,200 $1,400)
($1,400 $1,600)
($400 $600)
($600 $800)
($800 $1,000)
($1,600 ($1,800 $1,800)
$2,000)
Data Mining: Concepts and Techniques
($2,000 - $5, 000)
($2,000 $3,000)
($3,000 $4,000)
($4,000 $5,000)
89
Concept Hierarchy Generation for Categorical
Data

Specification of a partial/total ordering of attributes
explicitly at the schema level by users or experts


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


street < city < state < country
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}
April 13, 2017
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90
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
15 distinct values
country
province_or_ state
365 distinct values
city
3567 distinct values
street
April 13, 2017
674,339 distinct values
Data Mining: Concepts and Techniques
91
Chapter 2: Data Preprocessing

General data characteristics

Basic data description and exploration

Measuring data similarity

Data cleaning

Data integration and transformation

Data reduction

Summary
April 13, 2017
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92
Summary



Data preparation/preprocessing: A big issue for data mining
Data description, data exploration, and measure data
similarity set the base for quality data preprocessing
Data preparation includes

Data cleaning

Data integration and data transformation


Data reduction (dimensionality and numerosity
reduction)
A lot a methods have been developed but data
preprocessing still an active area of research
April 13, 2017
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References








D. P. Ballou and G. K. Tayi. Enhancing data quality in data warehouse environments.
Communications of ACM, 42:73-78, 1999
W. Cleveland, Visualizing Data, Hobart Press, 1993
T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley, 2003
T. Dasu, T. Johnson, S. Muthukrishnan, V. Shkapenyuk. Mining Database Structure; Or,
How to Build a Data Quality Browser. SIGMOD’02
U. Fayyad, G. Grinstein, and A. Wierse. Information Visualization in Data Mining and
Knowledge Discovery, Morgan Kaufmann, 2001
H. V. Jagadish et al., Special Issue on Data Reduction Techniques. Bulletin of the
Technical Committee on Data Engineering, 20(4), Dec. 1997
D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999
E. Rahm and H. H. Do. Data Cleaning: Problems and Current Approaches. IEEE Bulletin of
the Technical Committee on Data Engineering. Vol.23, No.4




V. Raman and J. Hellerstein. Potters Wheel: An Interactive Framework for Data Cleaning
and Transformation, VLDB’2001
T. Redman. Data Quality: Management and Technology. Bantam Books, 1992
E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001
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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Feature Subset Selection Techniques




Brute-force approach:
 Try all possible feature subsets as input to data mining
algorithm
Embedded approaches:
 Feature selection occurs naturally as part of the data
mining algorithm
Filter approaches:
 Features are selected before data mining algorithm is
run
Wrapper approaches:
 Use the data mining algorithm as a black box to find
best subset of attributes
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