CONCEPT DESCRIPTION: CHARACTERIZATION AND COMPARISON

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Topics:
 What is concept description?
 Data generalization and summarization-based
characterization
 Analytical characterization: Analysis of attribute relevance
 Mining class comparisons: Discriminating between
different classes
 Mining descriptive statistical measures in large databases
 Discussion
 Summary
What is Concept Description?
 Descriptive vs. predictive data mining
 Descriptive mining: describes concepts or task-relevant
data sets in concise, summarative, informative,
discriminative forms
 Predictive mining: Based on data and analysis,
constructs models for the database, and predicts the
trend and properties of unknown data
 Concept description:
 Characterization: provides a concise and succinct
summarization of the given collection of data
 Comparison: provides descriptions comparing two or
more collections of data
4
Concept Description vs. OLAP
 Concept description:
 can handle complex data types of the
attributes and their aggregations
 a more automated process
 OLAP:
 restricted to a small number of dimension and
measure types
 user-controlled process
5
DATA GENERALIZATION AND
SUMMARIZATION-BASED
CHARACTERIZATION
6
Data Generalization and Summarizationbased
Characterization
 Data generalization
 A process which abstracts a large set of task-relevant
data in a database from a low conceptual levels to
higher ones.
 Approaches:
 Data cube approach(OLAP approach)
 Attribute-oriented induction approach
1
2
3
4
5
Conceptual levels
7
Characterization: Data Cube Approach
 Data are stored in data cube
 Identify expensive computations
 e.g., count( ), sum( ), average( ), max( )
 Perform computations and store results in data
cubes
 Generalization and specialization can be
performed on a data cube by roll-up and drilldown
 An efficient implementation of data
generalization
8
Data Cube Approach (Cont…)
 Limitations
 can only handle data types of dimensions to
simple nonnumeric data and of measures to
simple aggregated numeric values .
 Lack of intelligent analysis, can’t tell which
dimensions should be used and what levels
should the generalization reach
9
Attribute-Oriented Induction
 Proposed in 1989 (KDD ‘89 workshop)
 Not confined to categorical data nor particular measures.
 How it is done?
 Collect the task-relevant data (initial relation) using a
relational database query
 Perform generalization by attribute removal or
attribute generalization.
 Apply aggregation by merging identical, generalized
tuples and accumulating their respective counts
 Interactive presentation with users
Basic Principles of Attribute-
Oriented Induction
 Data focusing: task-relevant data, including dimensions, and
the result is the initial relation.
 Attribute-removal: remove attribute A if there is a large set
of distinct values for A but (1) there is no generalization
operator on A, or (2) A’s higher level concepts are
expressed in terms of other attributes.
 Attribute-generalization: If there is a large set of distinct
values for A, and there exists a set of generalization
operators on A, then select an operator and generalize A.
 Attribute-threshold control: typical 2-8, specified/default.
 Generalized relation threshold control: control the final
relation/rule size. see example
Attribute-Oriented Induction: Basic Algorithm
 InitialRel: Query processing of task-relevant data, deriving
the initial relation.
 PreGen: Based on the analysis of the number of distinct
values in each attribute, determine generalization plan for
each attribute: removal? or how high to generalize?
 PrimeGen: Based on the PreGen plan, perform
generalization to the right level to derive a “prime
generalized relation”, accumulating the counts.
 Presentation: User interaction: (1) adjust levels by drilling,
(2) pivoting, (3) mapping into rules, cross tabs,
visualization presentations.
12
Example
 DMQL: Describe general characteristics of graduate
students in the Big-University database
use Big_University_DB
mine characteristics as “Science_Students”
in relevance to name, gender, major, birth_place,
birth_date, residence, phone#, gpa
from student
where status in “graduate”
 Corresponding SQL statement:
Select name, gender, major, birth_place, birth_date,
residence, phone#, gpa
from student
where status in “Msc”, “MBA”, “PhD”
Class Characterization: An Example
Name Gender Major Birth-Place Birth_date Residence Phone # GPA
Jim
Woodman
M CS Vancouver,BC,
Canada
8-12-76 3511 Main St.,
Richmond
687-4598 3.67
Scott
Lachance
M CS Montreal, Que,
Canada
28-7-75 345 1st Ave.,
Richmond
253-9106 3.70
Laura Lee

F

Physics

Seattle, WA, USA

25-8-70

125 Austin Ave.,
Burnaby

420-5232

3.83

Removed Retained Sci,Eng,
Bus
Country Age range City Removed Excl,
VG,..
Gender Major Birth_region Age_range Residence GPA Count
M Science Canada 20-25 Richmond Very-good 16
F Science Foreign 25-30 Burnaby Excellent 22
… … … … … … …
Birth_Region
Gender
Canada Foreign Total
M 16 14 30
F 10 22 32
Total 26 36 62
Prime
Generalized
Relation
Initial
Relation
Presentation of Generalized Results
 Generalized relation:
 Relations where some or all attributes are generalized, with counts
or other aggregation values accumulated.
 Cross tabulation:
 Mapping results into cross tabulation form (similar to contingency
tables).
 Visualization techniques:
 Pie charts, bar charts, curves, cubes, and other visual forms.
 Quantitative characteristic rules:
 Mapping generalized result into characteristic rules with quantitative
information associated with it, e.g.,
_ ( ) ” “[ :53%] _ ( ) ” “[ :47%].
( ) ( )
birth region x Canada t birth region x foreign t
grad x male x
  
 
15
Presentation—Generalized Relation
16
Presentation—Crosstab
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Implementation by Cube Technology
 Construct a data cube on-the-fly for the given data
mining query
 Facilitate efficient drill-down analysis
 May increase the response time
 A balanced solution: precomputation of “subprime”
relation
 Use a predefined & precomputed data cube
 Construct a data cube beforehand
 Facilitate not only the attribute-oriented induction, but
also attribute relevance analysis, dicing, slicing, rollup
and drill-down
 Cost of cube computation and the nontrivial storage
overhead
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ANALYTICAL CHARACTERIZATION:
ANALYSIS OF ATTRIBUTE
RELEVANCE
19
Characterization vs. OLAP
 Similarity:
 Presentation of data summarization at multiple levels of
abstraction.
 Interactive drilling, pivoting, slicing and dicing.
 Differences:
 Automated desired level allocation.
 Dimension relevance analysis and ranking when there
are many relevant dimensions.
 Sophisticated typing on dimensions and measures.
 Analytical characterization: data dispersion analysis.
20
Attribute Relevance Analysis
 Why?
 Which dimensions should be included?
 How high level of generalization?
 Automatic VS. Interactive
 Reduce # attributes; Easy to understand patterns
 What?
 statistical method for preprocessing data
 filter out irrelevant or weakly relevant attributes
 retain or rank the relevant attributes
 relevance related to dimensions and levels
 analytical characterization, analytical comparison
21
Attribute relevance analysis (cont’d)
 How?
 Data Collection
 Analytical Generalization
 Use information gain analysis (e.g., entropy or other
measures) to identify highly relevant dimensions and levels.
 Relevance Analysis
 Sort and select the most relevant dimensions and levels.
 Attribute-oriented Induction for class description
 On selected dimension/level
 OLAP operations (e.g. drilling, slicing) on relevance
rules
22
Relevance Measures
 Quantitative relevance measure determines the
classifying power of an attribute within a set of
data.
 Methods
 information gain (ID3)
 gain ratio (C4.5)
 gini index
 2 contingency table statistics
 uncertainty coefficient
23
Information-Theoretic Approach
 Decision tree
 each internal node tests an attribute
 each branch corresponds to attribute value
 each leaf node assigns a classification
 ID3 algorithm
 build decision tree based on training objects
with known class labels to classify testing
objects
 rank attributes with information gain measure
 minimal height
 the least number of tests to classify an object
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Top-Down Induction of Decision Tree
Attributes = Outlook, Temperature, Humidity, Wind
Outlook
Humidity Wind
sunny overcast rain
yes
no yes
high normal
no
strong weak
yes
PlayTennis = yes, no
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Entropy and Information Gain
 S contains si tuples of class Ci for i = 1, …, m
 Information measures info required to classify
any arbitrary tuple
 Entropy of attribute A with values a1,a2,…,av
 Information gained by branching on attribute A
s
log s
s
I( s ,s ,…,s ) s m i
i
i
1 2 m 2
1 
 
I( s ,…,s )
s
E(A) s … s j mj
v
j
j mj
1
1
1 

 

Gain(A)  I(s1,s 2,…,sm) E(A)
26
Example: Analytical Characterization
 Task
 Mine general characteristics describing graduate
students using analytical characterization
 Given
 attributes name, gender, major, birth_place, birth_date,
phone#, and gpa
 Gen(ai) = concept hierarchies on ai
 Ui = attribute analytical thresholds for ai
 Ti = attribute generalization thresholds for ai
 R = attribute relevance threshold
27
Example: Analytical Characterization (cont’d)
 1. Data collection
 target class: graduate student
 contrasting class: undergraduate student
 2. Analytical generalization using Ui
 attribute removal
 remove name and phone#
 attribute generalization
 generalize major, birth_place, birth_date and gpa
 accumulate counts
 candidate relation: gender, major, birth_country,
age_range and gpa
28
Example: Analytical characterization (2)
gender major birth_country age_range gpa count
M Science Canada 20-25 Very_good 16
F Science Foreign 25-30 Excellent 22
M Engineering Foreign 25-30 Excellent 18
F Science Foreign 25-30 Excellent 25
M Science Canada 20-25 Excellent 21
F Engineering Canada 20-25 Excellent 18
Candidate relation for Target class: Graduate students (=120)
gender major birth_country age_range gpa count
M Science Foreign <20 Very_good 18 F Business Canada <20 Fair 20 M Business Canada <20 Fair 22 F Science Canada 20-25 Fair 24 M Engineering Foreign 20-25 Very_good 22 F Engineering Canada <20 Excellent 24 Candidate relation for Contrasting class: Undergraduate students (=130) 29 Example: Analytical characterization (3)  3. Relevance analysis  Calculate expected info required to classify an arbitrary tuple  Calculate entropy of each attribute: e.g. major 0 9988 250 130 250 130 250 120 250 120130 120 I(s1,s 2 )  I( , )   log 2  log 2  . For major=”Science”: S11=84 S21=42 I(s11,s21)=0.9183 For major=”Engineering”: S12=36 S22=46 I(s12,s22)=0.9892 For major=”Business”: S13=0 S23=42 I(s13,s23)=0 Number of grad students in “Science” Number of undergrad students in “Science” 30 Example: Analytical Characterization (4)  Calculate expected info required to classify a given sample if S is partitioned according to the attribute  Calculate information gain for each attribute  Information gain for all attributes 0 7873 250 42 250 82 250 126 E(major)  I( s11,s21 ) I( s12,s22 ) I( s13,s23 )  . Gain(major)  I(s1,s 2 ) E(major) 0.2115 Gain(gender) = 0.0003 Gain(birth_country) = 0.0407 Gain(major) = 0.2115 Gain(gpa) = 0.4490 Gain(age_range) = 0.5971 31 Example: Analytical characterization (5)  4. Initial working relation (W0) derivation  R = 0.1  remove irrelevant/weakly relevant attributes from candidate relation => drop gender, birth_country
 remove contrasting class candidate relation
 5. Perform attribute-oriented induction on W0 using Ti
major age_range gpa count
Science 20-25 Very_good 16
Science 25-30 Excellent 47
Science 20-25 Excellent 21
Engineering 20-25 Excellent 18
Engineering 25-30 Excellent 18
Initial target class working relation W0: Graduate students
32
MINING CLASS COMPARISONS:
DISCRIMINATING BETWEEN
DIFFERENT CLASSES
Mining Class Comparisons
 Comparison: Comparing two or more classes
 Method:
 Partition the set of relevant data into the target class and the
contrasting class(es)
 Generalize both classes to the same high level concepts
 Compare tuples with the same high level descriptions
 Present for every tuple its description and two measures
 support – distribution within single class
 comparison – distribution between classes
 Highlight the tuples with strong discriminant features
 Relevance Analysis:
 Find attributes (features) which best distinguish different classes
34
Example: Analytical comparison
 Task
 Compare graduate and undergraduate students using
discriminant rule.
 DMQL query
use Big_University_DB
mine comparison as “grad_vs_undergrad_students”
in relevance to name, gender, major, birth_place, birth_date, residence, phone#, gpa
for “graduate_students”
where status in “graduate”
versus “undergraduate_students”
where status in “undergraduate”
analyze count%
from student
35
Example: Analytical comparison (2)
 Given
 attributes name, gender, major, birth_place,
birth_date, residence, phone# and gpa
 Gen(ai) = concept hierarchies on attributes ai
 Ui = attribute analytical thresholds for
attributes ai
 Ti = attribute generalization thresholds for
attributes ai
 R = attribute relevance threshold
36
Example: Analytical comparison (3)
 1. Data collection
 target and contrasting classes
 2. Attribute relevance analysis
 remove attributes name, gender, major, phone#
 3. Synchronous generalization
 controlled by user-specified dimension thresholds
 prime target and contrasting class(es)
relations/cuboids
37
Example: Analytical comparison (4)
Birth_country Age_range Gpa Count%
Canada 20-25 Good 5.53%
Canada 25-30 Good 2.32%
Canada Over_30 Very_good 5.86%
… … … …
Other Over_30 Excellent 4.68%
Prime generalized relation for the target class: Graduate students
Birth_country Age_range Gpa Count%
Canada 15-20 Fair 5.53%
Canada 15-20 Good 4.53%
… … … …
Canada 25-30 Good 5.02%
… … … …
Other Over_30 Excellent 0.68%
Prime generalized relation for the contrasting class: Undergraduate students
38
Example: Analytical comparison (5)
 4. Drill down, roll up and other OLAP operations
on target and contrasting classes to adjust levels
of abstractions of resulting description
 5. Presentation
 as generalized relations, crosstabs, bar charts,
pie charts, or rules
 contrasting measures to reflect comparison
between target and contrasting classes
 e.g. count%
39
Quantitative Discriminant Rules
 Cj = target class
 qa = a generalized tuple covers some tuples of class
 but can also cover some tuples of contrasting class
 d-weight
 range: [0, 1]
 quantitative discriminant rule form



  m
i
a i
a j
count(q C )
d weight count(q C )
1
 X, target_class(X)condition(X) [d : d_weight]
40
Example: Quantitative Discriminant Rule
 Quantitative discriminant rule
 where 90/(90+210) = 30%
Status Birth_country Age_range Gpa Count
Graduate Canada 25-30 Good 90
Undergraduate Canada 25-30 Good 210
Count distribution between graduate and undergraduate students for a generalized tuple
_ ( ) ” ” _ ( ) “25 30″ ( ) ” ” [ : 30%]
, _ ( )
birth country X Canada age range X gpa X good d
X graduate student X
     
 
41
Class Description
 Quantitative characteristic rule
 necessary
 Quantitative discriminant rule
 sufficient
 Quantitative description rule
 necessary and sufficient
1 [t :w1,d :w1]… [t :wn,d :wn]
 
condition (X) condition (X)
X, target_class(X)
n
 X, target_class(X)condition(X) [d : d_weight]
 X, target_class(X)condition(X) [t : t_weight]
42
Example: Quantitative Description Rule
 Quantitative description rule for target class Europe
Location/item TV Computer Both_items
Count t-wt d-wt Count t-wt d-wt Count t-wt d-wt
Europe 80 25% 40% 240 75% 30% 320 100% 32%
N_Am 120 17.65% 60% 560 82.35% 70% 680 100% 68%
Both_
regions
200 20% 100% 800 80% 100% 1000 100% 100%
Crosstab showing associated t-weight, d-weight values and total number
(in thousands) of TVs and computers sold at AllElectronics in 1998
(item(X) “TV” )t : 25%,d : 40%[t : 75%,d : 30%]
X,Europe(X)
  
 
43
Mining Complex Data Objects:
Generalization of Structured Data
 Set-valued attribute
 Generalization of each value in the set into its corresponding
higher-level concepts
 Derivation of the general behavior of the set, such as the
number of elements in the set, the types or value ranges in
the set, or the weighted average for numerical data
 E.g., hobby = tennis, hockey, chess, violin, nintendo_games
generalizes to sports, music, video_games
 List-valued or a sequence-valued attribute
 Same as set-valued attributes except that the order of the
elements in the sequence should be observed in the
generalization
44
Generalizing Spatial and Multimedia Data
 Spatial data:
 Generalize detailed geographic points into clustered regions,
such as business, residential, industrial, or agricultural areas,
according to land usage
 Require the merge of a set of geographic areas by spatial
operations
 Image data:
 Extracted by aggregation and/or approximation
 Size, color, shape, texture, orientation, and relative positions
and structures of the contained objects or regions in the image
 Music data:
 Summarize its melody: based on the approximate patterns that
repeatedly occur in the segment
 Summarized its style: based on its tone, tempo, or the major
musical instruments played
45
Generalizing Object Data
 Object identifier: generalize to the lowest level of class in the
class/subclass hierarchies
 Class composition hierarchies
 generalize nested structured data
 generalize only objects closely related in semantics to the current
one
 Construction and mining of object cubes
 Extend the attribute-oriented induction method
 Apply a sequence of class-based generalization operators on
different attributes
 Continue until getting a small number of generalized objects that
can be summarized as a concise in high-level terms
 For efficient implementation
 Examine each attribute, generalize it to simple-valued data
 Construct a multidimensional data cube (object cube)
 Problem: it is not always desirable to generalize a set of values to
single-valued data
46
An Example: Plan Mining by Divide & Conquer
 Plan: a variable sequence of actions
 E.g., Travel (flight):
 Plan mining: extraction of important or significant generalized
(sequential) patterns from a planbase (a large collection of plans)
 E.g., Discover travel patterns in an air flight database, or
 find significant patterns from the sequences of actions in the repair
of automobiles
 Method
 Attribute-oriented induction on sequence data
 A generalized travel plan: <small-big-small>  Divide & conquer:Mine characteristics for each subsequence  E.g., big: same airline, small-big: nearby region
47
A Travel Database for Plan Mining
 Example: Mining a travel planbase
plan# action# departure depart_time arrival arrival_time airline …
1 1 ALB 800 JFK 900 TWA …
1 2 JFK 1000 ORD 1230 UA …
1 3 ORD 1300 LAX 1600 UA …
1 4 LAX 1710 SAN 1800 DAL …
2 1 SPI 900 ORD 950 AA …
. . . . . . . .
. . . . . . . .
. . . . . . . .
airport_code city state region airport_size …
1 1 ALB 800 …
1 2 JFK 1000 …
1 3 ORD 1300 …
1 4 LAX 1710 …
2 1 SPI 900 …
. . . . .
. . . . .
. . . . .
Travel plans table
Airport info table
48
Multidimensional Analysis
 Strategy
 Generalize the
planbase in
different
directions
 Look for
sequential
patterns in the
generalized plans
 Derive high-level
plans
A multi-D model for the planbase
49
Multidimensional Generalization
Plan# Loc_Seq Size_Seq State_Seq
1 ALB – JFK – ORD – LAX – SAN S – L – L – L – S N – N – I – C – C
2 SPI – ORD – JFK – SYR S – L – L – S I – I – N – N
. . .
. . .
. . .
Multi-D generalization of the planbase
Plan# Size_Seq State_Seq Region_Seq …
1 S – L+ – S N+ – I – C+ E+ – M – P+ …
2 S – L+ – S I+ – N+ M+ – E+ …
. . .
. . .
. . .
Merging consecutive, identical actions in plans
( ) ( ) [75%]
( , ,) _ ( , ) _ ( , )
region x region y
flight x y airport size x S airport size y L
 
 
50
Generalization-Based Sequence Mining
 Generalize planbase in multidimensional way using
dimension tables
 Use # of distinct values (cardinality) at each level to
determine the right level of generalization
(level-“planning”)
 Use operators merge “+”, option “[]” to further generalize
patterns
 Retain patterns with significant support
51
Generalized Sequence Patterns
 AirportSize-sequence survives the min threshold (after
applying merge operator):
S-L+-S [35%], L+-S [30%], S-L+ [24.5%], L+ [9%]
 After applying option operator:
[S]-L+-[S] [98.5%]
 Most of the time, people fly via large airports to get to
final destination
 Other plans: 1.5% of chances, there are other patterns:
S-S, L-S-L
52
MINING DESCRIPTIVE
STATISTICAL MEASURES IN LARGE
DATABASES
53
Mining Data Dispersion 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
54
Measuring the Central Tendency
 Mean
 Weighted arithmetic mean
 Median: A holistic measure
 Middle value if odd number of values, or average of the
middle two values otherwise
 estimated by interpolation
 Mode
 Value that occurs most frequently in the data
 Unimodal, bimodal, trimodal
 Empirical formula:


n
i
i x
n
x
1
1



 n
i
i
n
i
i i
w
w x
x
1
1
c
f
n f l
median L
median
)
/ 2 ( )
( 1
 
 
mean mode  3(mean median)
55
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
 Variance s2: (algebraic, scalable computation)
 Standard deviation s is the square root of variance s2
  
  


 


n
i
n
i
i i
n
i
i x
n
x
n
x x
n
s
1 1
2 2
1
2 2 [ 1 ( ) ]
1
( ) 1
1
1
56
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
57
Visualization of Data Dispersion: Boxplot Analysis
58
Mining Descriptive Statistical Measures in Large
Databases
 Variance
 Standard deviation: the square root of the variance
 Measures spread about the mean
 It is zero if and only if all the values are equal
 Both the deviation and the variance are algebraic
  


 

 

   

2 2
1
2 2 1
1
( ) 1
1
1
i i
n
i
i x
n
x
n
x x
n
s
59
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
60
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
61
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
62
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
63
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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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
 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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DISCUSSION
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AO(Attribute Oriented) Induction vs.
Learning-from-example Paradigm
 Difference in philosophies and basic assumptions
 Positive and negative samples in learning-fromexample:
positive used for generalization, negative –
for specialization
 Positive samples only in data mining: hence
generalization-based, to drill-down backtrack the
generalization to a previous state
 Difference in methods of generalizations
 Machine learning generalizes on a tuple by tuple basis
 Data mining generalizes on an attribute by attribute
basis
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Entire vs. Factored Version Space
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Incremental and Parallel Mining of Concept
Description
 Incremental mining: revision based on newly added data
DB
 Generalize DB to the same level of abstraction in the
generalized relation R to derive R
 Union R U R, i.e., merge counts and other statistical
information to produce a new relation R’
 Similar philosophy can be applied to data sampling,
parallel and/or distributed mining, etc.
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Summary
 Concept description: characterization and discrimination
 OLAP-based vs. attribute-oriented induction
 Efficient implementation of AOI
 Analytical characterization and comparison
 Mining descriptive statistical measures in large
databases
 Discussion
 Incremental and parallel mining of description
 Descriptive mining of complex types of data
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Thank you !!!
Questions
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Explain analytical characterization?
Methods of attribute relevance analysis?
How does analytical data
characterization/comparison performs?
From the descriptive statistics point of view, why is it
that additional statistical measures should be
introduced in describing central tendency and data
dispersion? Give an example.
In comparison with machine learning algorithm, why
is it that database-oriented concept description leads
to efficiency and scalability in large databases and
data warehouse?
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Discuss why analytical data characterization is needed and
how it can be performed. Compare the result of two
induction methods with relevance analysis and without
relevance analysis.
Give three additional commonly used statistical measures
for the characterization of data dispersion and discuss how
they can be computed efficiently in large databases.

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