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Akuisisi, Integrasi Akuisisi, Integrasi dan Eksplorasi dan Eksplorasi
DataData
Bahan 2013::::Dr.Eng. Taufik Djatna Darajat, STP, MSi1
What is Data?Collection of data objects and
their attributesAn attribute is a property or
characteristic of an object Examples: eye color of a person,
temperature, etc. Attribute is also known as
variable, field, characteristic, or feature
A collection of attributes describe an object Object is also known as record,
point, case, sample, entity, or instance
Collection of data objects and their attributes
An attribute is a property or characteristic of an object Examples: eye color of a person,
temperature, etc. Attribute is also known as
variable, field, characteristic, or feature
A collection of attributes describe an object Object is also known as record,
point, case, sample, entity, or instance
Tid Refund Marital Status
Taxable Income Cheat
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes 10
Attributes
Objects
Attribute valuesAttribute values are numbers or symbols
assigned to an attribute
Distinction between attributes and attribute valuesSame attribute can be mapped to different attribute
values Example: height can be measured in feet or meters
Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are
integers But properties of attribute values can be different
ID has no limit but age has a maximum and minimum value3
4
Tipe atribut Deskripsi Contoh
Kategori (kualitatif)
Nominal Nilai dari atribut nominal adalah nama-nama yang berbeda, yaitu nilai nominal hanya menyediakan informasi yang cukup untuk membedakan satu objek dengan objek yang lain. (= dan )
Kode pos, ID Number karyawan, warna mata, jenis kelamin
Ordinal Nilai dari atribut ordinal menyediakan informasi yang cukup mengurutkan objek. (<, >)
Nomor jalan, grade
Numerik (Kuantitatif)
Interval Untuk atribut interval, perbedaan antarnilai adalah sesuatu yang berarti, adanya unit pengukuran. (+,)
Tanggal pada kalender
Ratio Untuk variabel rasio, perbedaan dan rasio merupakan hal yang berarti. (*, /)
Kuantitas moneter, count, umur, panjang, arus listrik
Tipe-tipe atribut yang berbeda
Discrete and Continuous Attributes Discrete Attribute
Has only a finite or count ably infinite set of values Examples: zip codes, counts, or the set of words in a collection
of documents Often 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.
5
Types of data sets
Record Data Matrix Document Data Transaction Data
Graph World Wide Web Molecular Structures
Ordered Spatial Data Temporal Data Sequential Data Genetic Sequence Data6
Record Data Data that consists of a collection of records, each of which
consists of a fixed set of attributes
Tid Refund Marital Status
Taxable Income Cheat
1 Yes Single 125K No
2 No Married 100K No
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K Yes
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes 10
7
Transaction Data A special type of record data, where
each record (transaction) involves a set of items. For example, consider a grocery store. The set of products
purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.
TID Items
1 Bread, Coke, Milk
2 Beer, Bread
3 Beer, Coke, Diaper, Milk
4 Beer, Bread, Diaper, Milk
5 Coke, Diaper, Milk
8
Ordered DataTemporal Data • Temporal data is data
whose objects have attributes that represent measurements taken over time.
• For example, financial data set are time series that give the daily prices of various stocks.
• A time series is a sequence of measurements of some attribute
• e.g., stock price or rainfall, taken at (usually regular) points in time.)
9
Ordered Data Sequences of transactions
An element of the sequence
Items/Events
The data still consists of a set of transactions and items, but time and customer ID attributes are associated with each transaction.
10
Data Quality What kinds of data quality problems?How can we detect problems with the
data? What can we do about these problems?
Examples of data quality problems: Noise and outliers missing values duplicate data
What kinds of data quality problems?How can we detect problems with the
data? What can we do about these problems?
Examples of data quality problems: Noise and outliers missing values duplicate data
11
Noise Noise refers to modification of original values
Examples: distortion of a person’s voice when talking on a poor phone and “snow” on television screen
Two Sine Waves Two Sine Waves + Noise12
Outliers Outliers are data objects with characteristics that are
considerably different than most of the other data objects in the data set
13
Missing Values Reasons for missing values
Information is not collected (e.g., people decline to give their age and weight)
Attributes may not be applicable to all cases (e.g., annual income is not applicable to children)
Handling missing values Eliminate Data Objects Estimate Missing Values the missing values can be estimated
(interpolated) by using the remaining values. Ignore the Missing Value During Analysis
e.g., suppose that objects are being clustered and the similarity between pairs of data objects.
the similarity can be calculated by using only the non-missing attributes
14
Duplicate DataData set may include data objects that are
duplicates, or almost duplicates of one anotherMajor issue when merging data from heterogonous
sourcesExamples: Same person with multiple email addresses
That care needs to be taken to avoid accidentally combining data objects that are similar, but not duplicates.
Data cleaningProcess of dealing with duplicate data issues
15
07/05/08
SUMBER DATASUMBER DATA
1.Transaksi Sistem operasi:: 2.Data Warehouse dan Datamart3.Aplikasi-aplikasi OLAP / Business
Intelligence4.Survey
1.Jumlah partisipan2.Bentuk kuisioner langsung ke ide
5.Database Rumah tangga dan Demografik
Data Pre-processingData Pre-processingAggregationAggregationSamplingSamplingDimensionality ReductionDimensionality ReductionFeature subset selectionFeature subset selectionFeature creationFeature creationDiscretization and BinarizationDiscretization and BinarizationAttribute TransformationAttribute Transformation
17
AggregationCombining two or more attributes (or objects)
into a single attribute (or object)Purpose
Data reduction Reduce the number of attributes or objects
Change of scale Cities aggregated into regions, states, countries, etc
More “stable” data Aggregated data tends to have less variability
18
Several motivations for aggregationSmaller data sets require less memory and
processing time.if the aggregation is done properly then the
aggregation can act as a change of scope or scale, providing a high level view of the data instead of a low level view.
the behaviour of groups of objects or attributes is often more ‘stable’ that that of individual objects or attributes.
19
Sampling Sampling is the main technique employed for data
selection. It is often used for both the preliminary investigation of the
data and the final data analysis.
Statisticians sample because obtaining the entire set of data of interest is too expensive or time consuming.
Sampling is used in data mining because processing
the entire set of data of interest is too expensive or time consuming.
20
Sampling … The key principle for effective sampling is the
following: using a sample will work almost as well as using the
entire data sets, if the sample is representative
A sample is representative if it has approximately the same property (of interest) as the original set of data
if the standard deviation is the property of interest, then a sample is representative if the sample has a standard deviation that is close to that of the original data 21
Types of SamplingSimple Random Sampling
There is an equal probability of selecting any particular item
Sampling without replacementAs each item is selected, it is removed from the
population
Sampling with replacementObjects are not removed from the population as they
are selected for the sample. In sampling with replacement, the same object can be picked
up more than once22
Sample Size
8000 points 2000 Points 500 Points
• Larger sample sizes increase the probability that a sample will be representative, but also eliminate much of the advantage of sampling.
• Conversely, with smaller sample sizes, patterns may be missed or erroneous patterns detected.
23
Dimensionality ReductionDimensionality ReductionPurpose:Reduce amount of time and memory
required by data mining algorithmsAllow data to be more easily visualizedMay help to eliminate irrelevant
features or reduce noise
TechniquesPrinciple Component Analysis
24
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 Example: 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 Example: students' ID is often irrelevant to the task of predicting
students' GPA
25
26
Feature Selection for Correlated Feature Selection for Correlated Numerical MeasurementNumerical Measurement
27
F1 F2 F3 … FM C
I1 60 0 21 .. 61
I2 10 1 30 .. 51
I3 31 10 1 .. 41
I4 2 11 0 .. 1
.. .. … .. … .. …
.. .. .. .. … .. …
… … .. .. … … …
IN 21 21 30 … 50
Goal:Goal:Find the most compact and Find the most compact and informative attributesinformative attributes
Target Class Type (C) in Numerical Database:
Categorical Classification
Continues Regression
Attribute Selection Problem DefinitionAttribute Selection Problem Definition
07/05/08
Pelatihan Teknik Dasar Data mining dan Data Warehousing
29
Conventional Attribute Selection TechniqueConventional Attribute Selection Technique
Distance Based :
RELIEF (Kira and Rendel 1992)
Correlation Based:
CFS (Hall, 1998)
30
Distance Based : ReliefDistance Based : Relief
nearest hit (H) between same classnearest miss (M) between different class
Hhit
Mmiss
Hhit
Mmiss
Wx1= (-10/10+6/10 -1/10+5/10
-3/10+2/10
Wx2= (-3/9+1/9 -5/9+2/9
-4/9+4/9
Max x1=12, min x1=2 range =10
Max x2=14, min x2=5range=9
Sample =5
Wx1 > Wx2 select x1
X1 X2 Class
2 5
8 7
5 10
12 10
6 14
2 1285 6
5
7
10
14
-1/10+6/10
-10/10+4/10)/5= -0.04
-5/9+3/9
-4/9+4/9)/5= -0.133
31
Correlation Based : CFSCorrelation Based : CFS
1
;( 1)
; ( ) 2
# . 2
1 attribute
0
cfF
f f
k
XY xy i biix y
ibi
krScore
k k k r
xyr r p X x rX Y
k cat value k
X xX binary
otherwise
(2,5)
(8,7)
(12,10)(5,10)
(6,14)Sum(x1.x2)=(2*5+ ….+ 6*14)= 310,
x1=3.71; x2=4.08
rx1x2=310/(2(3.71*4.08))= 29.14
rx1.class=2/5(2+0+0+12+0)+ 3/5(0+8+5+0+6)=85/5= 17
rx2.class=2/5(5+0+0+10+0)+ 3/5(0+7+10+0+14) =123/5= 24.6
Score x2> Score x1 Select x2
2 5 6 8 12
5
7
10
14
X1 X2 Class
2 5
8 7
5 10
12 10
6 14
1
2
2*17
2 2(2 1)29.14
2*24.6
2 2(2 1)29.14
4.38
6.34
X
X
Score
Score
32
2.1.4 Problem of Conventional Methods 2.1.4 Problem of Conventional Methods healthy
Ignore the existence of correlated attributesNo mechanism to revive correlated attributesHard to find knowledge in correlated attributes
Wei
ght
(kg)
Height (cm)140 150 160 170
50
40
60
70
Target attribute / (health status) is
only decided by both attributes
Mapping
Data in two dimension
HOME WORK
Compute which attribute the most relevant one to classify the target?, compare Relief`
a1 a2 a3 a4 target
2 3 2 1 Star
3 4 4 1 Circle
4 3 3 16 Star
1 4 7 5 Circle
1 3 9 9 Star
1 2 3 6 Star
3 2 2 0 Star
4 2 1 6 Circle
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 the creation of a new, smaller set of features from the original set of
features. E.g., consider a set of photographs, where each photograph is to be
classified according to whether it contains a human face or not Mapping Data to New Space e.g. for each time series, the Fourier transform produces a
new data object, whose attributes are related to frequencies. Feature Construction
combining features one or more new features constructed out of the original features may
be more useful34
Attribute Transformation
A function that maps the entire set of values of a given attribute to a new set of replacement values such that each old value can be identified with one of the new valuesSimple functions: xk, |x|Standardization and Normalization
35
Summary StatisticsSummary statistics are numbers that
summarize properties of the data
Summarized properties include frequency, location and spread Examples: location - mean
spread - standard deviation
Most summary statistics can be calculated in a single pass through the data
Frequency and ModeThe frequency of an attribute value is the
percentage of time the value occurs in the data set For example, given the attribute ‘gender’ and a
representative population of people, the gender ‘female’ occurs about 50% of the time.
The mode of a an attribute is the most frequent attribute value
The notions of frequency and mode are typically used with categorical data
Measures of Location: Mean and Median The mean is the most common measure of the location of
a set of points. However, the mean is very sensitive to outliers. Thus, the median or a trimmed mean is also commonly
used.
Measures of Spread: Range and Variance
Range is the difference between the max and min The variance or standard deviation is the most common
measure of the spread of a set of points.
However, this is also sensitive to outliers, so that other measures are often used.
Visualization Visualization is the conversion of data into a
visual or tabular format so that the characteristics of the data and the relationships among data items or attributes can be analysed or reported.
Visualization of data is one of the most powerful and appealing techniques for data exploration. Can detect general patterns and trendsCan detect outliers and unusual patterns
Example: Sea Surface Temperature The following shows the Sea Surface Temperature (SST) for
July 1982 Tens of thousands of data points are summarized in a single
figure
RepresentationIs the mapping of information to a visual formatData objects, their attributes, and the
relationships among data objects are translated into graphical elements such as points, lines, shapes, and colors.
Example: Objects are often represented as pointsTheir attribute values can be represented as the
position of the points or the characteristics of the points, e.g., color, size, and shape
If position is used, then the relationships of points, i.e., whether they form groups or a point is an outlier, is easily perceived.
Arrangement Is the placement of visual elements within a display Can make a large difference in how easy it is to understand
the data Example:
Visualization Techniques: Histograms Histogram
Usually shows the distribution of values of a single variable Divide the values into bins and show a bar plot of the number of objects in
each bin. The height of each bar indicates the number of objects Shape of histogram depends on the number of bins
Example: Petal Width (10 and 20 bins, respectively)
Two-Dimensional Histograms Show the joint distribution of the values of two attributes Example: petal width and petal length
What does this tell us?
Visualization Techniques: Box Plots Box Plots
Invented by J. Tukey Another way of displaying the distribution of data Following figure shows the basic part of a box plot
outlier
10th percentile
25th percentile
75th percentile
50th percentile
10th percentile
Example of Box Plots Box plots can be used to compare attributes
Visualization Techniques: Scatter Plots Scatter plots
Attributes values determine the position Two-dimensional scatter plots most common, but can have
three-dimensional scatter plots Often additional attributes can be displayed by using the size,
shape, and color of the markers that represent the objects It is useful to have arrays of scatter plots can compactly
summarize the relationships of several pairs of attributes See example on the next slide
Scatter Plot Array of Iris Attributes
OLAPOn-Line Analytical Processing (OLAP) was
proposed by E. F. Codd, the father of the relational database.
Relational databases put data into tables, while OLAP uses a multidimensional array representation. Such representations of data previously existed in
statistics and other fieldsThere are a number of data analysis and data
exploration operations that are easier with such a data representation.
Creating a Multidimensional ArrayTwo key steps in converting tabular data into a
multidimensional array. First, identify which attributes are to be the dimensions and
which attribute is to be the target attribute whose values appear as entries in the multidimensional array. The attributes used as dimensions must have discrete
values The target value is typically a count or continuous value,
e.g., the cost of an item Can have no target variable at all except the count of
objects that have the same set of attribute values Second, find the value of each entry in the multidimensional
array by summing the values (of the target attribute) or count of all objects that have the attribute values corresponding to that entry.
Example: Iris dataWe show how the attributes, petal length, petal width,
and species type can be converted to a multidimensional array First, we discretized the petal width and length to have
categorical values: low, medium, and high We get the following table - note the count attribute
Example: Iris data (continued) Each unique tuple of petal width, petal length, and species
type identifies one element of the array. This element is assigned the corresponding count value. The figure illustrates
the result. All non-specified
tuples are 0.
Example: Iris data (continued) Slices of the multidimensional array are shown by the
following cross-tabulations What do these tables tell us?
OLAP Operations: Data CubeThe key operation of a OLAP is the formation of a data
cubeA data cube is a multidimensional representation of
data, together with all possible aggregates.By all possible aggregates, we mean the aggregates
that result by selecting a proper subset of the dimensions and summing over all remaining dimensions.
For example, if we choose the species type dimension of the Iris data and sum over all other dimensions, the result will be a one-dimensional entry with three entries, each of which gives the number of flowers of each type.
Consider a data set that records the sales of products at a number of company stores at various dates.
This data can be represented as a 3 dimensional array
There are 3 two-dimensionalaggregates (3 choose 2 ),3 one-dimensional aggregates,and 1 zero-dimensional aggregate (the overall total)
Data Cube Example
The following figure table shows one of the two dimensional aggregates, along with two of the one-dimensional aggregates, and the overall total
Data Cube Example (continued)
OLAP Operations: Slicing and DicingSlicing is selecting a group of cells from the
entire multidimensional array by specifying a specific value for one or more dimensions.
Dicing involves selecting a subset of cells by specifying a range of attribute values. This is equivalent to defining a subarray from the
complete array. In practice, both operations can also be
accompanied by aggregation over some dimensions.
OLAP Operations: Roll-up and Drill-downAttribute values often have a hierarchical
structure.Each date is associated with a year, month, and week.A location is associated with a continent, country,
state (province, etc.), and city. Products can be divided into various categories, such
as clothing, electronics, and furniture.Note that these categories often nest and form a
tree or latticeA year contains months which contains dayA country contains a state which contains a city
OLAP Operations: Roll-up and Drill-down
This hierarchical structure gives rise to the roll-up and drill-down operations.For sales data, we can aggregate (roll up) the sales
across all the dates in a month. Conversely, given a view of the data where the time
dimension is broken into months, we could split the monthly sales totals (drill down) into daily sales totals.
Likewise, we can drill down or roll up on the location or product ID attributes.
References
Chakrabarti, S., et.a l. 2009. Data Mining know It All. Morgan Kaufmann Publishers. Elsevier, Amsterdam.
Tan P., Michael S., & Vipin K. 2006. Introduction to Data mining. Pearson Education, Inc.
Han J & Kamber M. 2012. Data mining – Concept and Techniques.Morgan-Kauffman, San Diego
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