Magister Teknologi Informasi - PowerPoint Presentation

Slide1

Magister Teknologi InformasiUniversitas Indonesia2012

Data Mining

Slide2

More data is generated:Bank, telecom, other business transactions ...

Scientific Data: astronomy, biology, etc

Web, text, and e-commerce

More data is captured:Storage technology faster and cheaperDBMS capable of handling bigger DB

Slide3

We have large data stored in one or more database/s.We

starved

to find new

information within those data (for research usage, competitive edge, etc).We want to identify patterns or rules (trends and relationships) in those data.We know that a certain data exist inside a database, but what are the consequences of that data’s existence?

Slide4

There is often information “hidden

”

in the data that is

not readily evidentHuman analysts may take weeks to discover useful informationMuch of the data is never analyzed at all

The Data Gap

Total new disk (TB) since 1995

Number of analysts

From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”

Slide5

Data Mining is a process of extracting previously unknown, valid and

actionable

information from large databases and then using the information to make crucial

business decisions (Cabena et al. 1998).Data mining: discovering interesting patterns from large amounts of data (Han and Kamber, 2001).

Slide6

Definition from [Connolly, 2005]:The process of extracting valid,

previously unknown

, comprehensive, and actionable information from large databases and using it to make crucial business decisions.

The thin red line of data mining: it is all about finding patterns or rules by extracting data from large databases in order to find new information that could lead to new knowledge.

Slide7

What is Data Mining?

Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area)

Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,)

What is not Data Mining?

Look up phone number in phone directory

Query a Web search engine for information about “Amazon”

Slide8

__

____

__

____

__

____

Transformed

Data

Patterns

and

Rules

Target

Data

RawData

Knowledge

Data Mining

Transformation

Interpretation

& Evaluation

Selection

& Cleaning

Integration

Understanding

DATA

Ware

house

Knowledge

Data Mining in Knowledge Discovery Process

Slide9

ClusteringClassification

Association Rules

Other Methods:

Outlier detectionSequential patternsPrediction

Trends and analysis of changes

Methods for special data types, e.g., spatial data mining, web mining

…

Slide10

Association rules try to find association between items in a set of transactions.For example, in the case of association between items bought by customers in supermarket:

90% of transactions that purchase bread and butter also purchase milk

Antecedent: bread and butter Consequent: milk

Confidence factor:

90%

Slide11

A transaction is a set of items: T={ia, i

b

,…i

t}T ⊂ I, where I is the set of all possible items {i1

, i

2

,…i

n

}

D

, the task relevant data, is a set of transactions (database of transactions).

Example:

items sold by supermarket (I:Itemset):

{sugar, parsley, onion, tomato, salt, bread, olives, cheese, butter}

Transaction by customer (T):

T1:

{sugar, onion, salt}

Database (D):

{T1={salt, bread, olives}, T2={sugar, onion, salt}, T3={bread}, T4={cheese, butter}, T5={tomato}, …}

Slide12

An association rule is the form: P → Q, where P⊂

I

, Q ⊂

I, and P ∩ Q = ∅Example:{bread} → {butter, cheese}

{onion, tomato} → {salt}

Slide13

Support of a rule P → Q = Support of (P

∪

Q

) in DsD(P → Q

) =

s

D

(

P

∪

Q

)

percentage of transactions in

D

containing P and Q

.

#transactions containing

P

and

Q

divided by cardinality of

D

.

Confidence of a rule P → Q

c

D

(

P → Q

) =

s

D

(

P

∪

Q

)/

s

D

(

P

)

percentage of transactions that contain both

P

and

Q

in the subset of transactions that contain already

P

.

Slide14

Thresholds:minimum support:

minsup

minimum confidence:

minconfFrequent itemset Psupport of P larger than minimum support

Strong rule P → Q (

c

%)

(

P

∪

Q

) frequent,

c

is larger than minimum confidence

Slide15

For rule {A} → {

C

}:

support = support({A, C}) = 50%confidence = support({A

,

C

})/support({

A

}) = 66.6%

For rule {

C

} → {

A

}:

support = support({A,

C

}) = 50%

confidence = support({

A

,

C

})/support({

C

}) = 100.0%

Transaction ID

Items Bought

2000

A, B, C

1000

A, C

4000

A, D

5000

B, E, F

Frequent Itemset

Support

{A}

75%

{B}

50%

{C}

50%

{A,C}

50%

Min. support 50%

Min. confidence 50%

Slide16

InputA database of transactions

Each transaction is a list of items (Ex. purchased by a customer in a visit)

Find

all strong rules that associate the presence of one set of items with that of another set of items.Example: 98% of people who purchase tires and auto accessories also get automotive services done

There are no restrictions on the number of items in the head or body of the rule.

The most famous algorithm is APRIORI

Slide17

Find the frequent itemsets: the sets of items that have minimum support A subset of a frequent itemset must also be a frequent itemset

i.e., if {AB} isa frequent itemset, both {A} and {B} should be a frequent itemset

Iteratively find frequent itemsets with cardinality from 1 to k (k-itemset)

Use the frequent itemsets to generate association rules.Source: [Sunysb, 2009]

Slide18

Consider a database, D, consisting of 9 transactions.Suppose min. support count required is 2 (i.e.

min_sup = 2/9 = 22%

).

Let minimum confidence required is 70%.We have to first find out the frequent itemset using apriori algorithm.Then, association rules will be generated using min. support & min. confidence

TID

List of Items

T100

I1, I2, I5

T100

I2, I4

T100

I2, I3

T100

I1, I2, I4

T100

I1, I3

T100

I2, I3

T100

I1, I3

T100

I1, I2 ,I3, I5

T100

I1, I2, I3

Slide19

Step 1: Generating 1-itemset Frequent Pattern

Itemset

Sup.Count

{l1}

6

{l2}

7

{l3}

6

{l4}

2

{;5}

2

Itemset

Sup.Count

{l1}

6

{l2}

7

{l3}

6

{l4}

2

{;5}

2

Scan D for count of each candidate

Compare candidate support count with minimum support count

C

1

L

1

The set of frequent 1-itemsets, L1

, consists of the candidate 1-itemsets satisfying minimum support.

In the first iteration of the algorithm, each item is member of the set of candidate

Slide20

Step 2: Generating 2-itemset Frequent Pattern

Itemset

{l1,l2}

{l1,l3}

{l1,l4}

{l1,l5}

{l2,l3}

{l2,l4}

{l2,l5}

{l3,l4}

{l3,l5}

{l4,l5}

Itemset

Sup.

Count

{l1,l2}

4

{l1,l3}

4

{l1,l4}

1

{l1,l5}

2

{l2,l3}

4

{l2,l4}

2

{l2,l5}

2

{l3,l4}

0

{l3,l5}

1

{l4,l5}

0

Itemset

Sup.

Count

{l1,l2}

4

{l1,l3}

4

{l1,l5}

2

{l2,l3}

4

{l2,l4}

2

{l2,l5}

2

Generate C

2

candidates from L

1

Scan D for count of each candidate

Compare candidate support count with minimum support count

C

2

C

2

L

2

Slide21

To discover the set of frequent 2-itemsets, L2, the algorithm uses

L

1

JoinL1 to generate a candidate set of 2-itemsets, C2.Next, the transactions in D are scanned and the support count for each candidate itemset in C

2

is accumulated (as shown in the middle table).

The set of frequent 2-itemsets, L

2

, is then determined, consisting of those candidate 2-itemsets in C

2

having minimum support.

Note

:We haven’t used Apriori Property yet.

Slide22

Step 3: Generating 3-itemset Frequent Pattern

Itemset

{l1,l2,l3}

{l1,l2,l5}

Itemset

Sup.

Count

{l1,l2,l3}

2

{l1,l2,l5}

2

Generate C

3

candidates from L

2

Scan D for count of each candidate

Compare candidate support count with minimum support count

C

3

C

3

L

3

Itemset

Sup.

Count

{l1,l2,l3}

2

{l1,l2,l5}

2

Slide23

The generation of the set of candidate 3-itemsets, C3, involves

use of the Apriori Property

.

In order to find C3, we compute L2JoinL2.

C

3

= L

2

JoinL

2

= {{I1, I2, I3}, {I1, I2, I5}, {I1, I3, I5}, {I2, I3, I4}, {I2, I3, I5}, {I2, I4, I5}}.

Slide24

Based on the Apriori property that all subsets of a frequent itemset must also be frequent, we can determine that four latter candidates cannot possibly be frequent.

For example, lets take

{I1, I2, I3}

. The 2-item subsets of it are {I1, I2}, {I1, I3} & {I2, I3}. Since all 2-item subsets of {I1, I2, I3} are members of L2, We will keep {I1, I2, I3} in C3. Lets take another example of

{I2, I3, I5}

which shows how the pruning is performed. The 2-item subsets are {I2, I3}, {I2, I5} & {I3,I5}.

Slide25

BUT, {I3, I5} is not a member of L2 and hence it is not frequent

violating Apriori Property.

Thus We will have to remove {I2, I3, I5} from C

3. Therefore, C3= {{I1, I2, I3}, {I1, I2, I5}} after checking for all members of result of Join operation for

Pruning

.

Now, the transactions in D are scanned in order to determine L

3

, consisting of those candidates 3-itemsets in C having minimum support.

Slide26

Step 4: Generating 4-itemset Frequent PatternThe algorithm uses

L

3

JoinL3 to generate a candidate set of 4-itemsets, C4. Although the join results in {{I1, I2, I3, I5}}, this itemset is pruned since its subset {{I2, I3, I5}} is not frequent. Thus,

C4= φ

, and algorithm terminates,

having found all of the frequent items. This completes our Apriori Algorithm

.

What’s Next ? These frequent itemsets will be used to generate

strong association rules

( where strong association rules satisfy both minimum support & minimum confidence).

Slide27

Step 5: Generating Association Rules from Frequent ItemsetsProcedure

:

For each frequent itemset “I”, generate all nonempty subsets of I.

For every nonempty subset s of I, output the rule “s

➔

(I-s)

” if

support_count(I) / support_count(s) ≥ min_conf

where min_conf is minimum confidence threshold.

Slide28

In our example:We had L = {{l1},{l2},{l3},{l4},{l5},{l1,l2},{l1,l3},{l1,l5},{l2,l3},{l2,l3},{l2,l5},{l1,l2,l3},{l1,l2,l5}}.

Lets take

I

= {l1,l2,l5}Its all nonempty subsets are {l1,l2}, {l1,l5}, {l2,l5}, {l1}, {l2}, {l5}

Slide29

Let minimum confidence thresholdis , say 70%.The resulting association rules are shown below, each listed with its confidence.

R1: {I1,I2}

➔

{I5}Confidence = sc{I1,I2,I5}/sc{I1,I2} = 2/4 = 50% R1 is Rejected.R2: {I1,I5}

➔

{I2}

Confidence = sc{I1,I2,I5}/sc{I1,I5} = 2/2 = 100%

R2 is Selected

.

R3: {I2,I5}

➔

{I1}

Confidence = sc{I1,I2,I5}/sc{I2,I5} = 2/2 = 100%

R3 is Selected

.

Slide30

R4: {I1} ➔ {I2,I5}

Confidence = sc{I1,I2,I5}/sc{I1} = 2/6 = 33%

R4 is Rejected.

R5: {I2} ➔ {I1,I5}

Confidence = sc{I1,I2,I5}/{I2} = 2/7 = 29%

R5 is Rejected.

R6: {I5}

➔

{I1,I2}

Confidence = sc{I1,I2,I5}/ {I5} = 2/2 = 100%

R6 is Selected

.

In this way, We have found three strong association rules.

Slide31

Learn a method for predicting the instance class from pre-labeled (classified) instances

Many approaches: Statistics,

Decision Trees, Neural Networks,

...

Slide32

Prepare a collection of records (training set )

Each record contains a set of

attributes

, one of the attributes is the class.Find a model for class attribute as a function of the values of other attributes (decision tree, neural network, etc)

Prepare

test set

to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

After happy with the accuracy, use your model to classify new instance

From: http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Slide33

categorical

categorical

continuous

class

Test

Set

Training

Set

Model

Learn

Classifier

From: http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Slide34

categorical

categorical

continuous

class

Refund

MarSt

TaxInc

YES

NO

NO

NO

Yes

No

Married

Single, Divorced

< 80K

> 80K

Splitting Attributes

Training Data

Model: Decision Tree

Slide35

categorical

categorical

continuous

class

MarSt

Refund

TaxInc

YES

NO

NO

NO

Yes

No

Married

Single, Divorced

< 80K

> 80K

There could be more than one tree that fits the same data!

Slide36

Refund

MarSt

TaxInc

YES

NO

NO

NO

Yes

No

Married

Single, Divorced

< 80K

> 80K

Test Data

Start from the root of tree.

Slide37

Direct MarketingGoal: Reduce cost of mailing by

targeting

a set of consumers likely to buy a new cell-phone product.

Approach:Use the data for a similar product introduced before. We know which customers decided to buy and which decided otherwise. This {buy, don’t buy} decision forms the

class attribute

.

Collect various demographic, lifestyle, and company-interaction related information about all such customers.

Type of business, where they stay, how much they earn, etc.

Use this information as input attributes to learn a classifier model.

From [Berry & Linoff] Data Mining Techniques, 1997

Slide38

Fraud DetectionGoal: Predict fraudulent cases in credit card transactions.

Approach:

Use credit card transactions and the information on its account-holder as attributes.

When does a customer buy, what does he buy, how often he pays on time, etcLabel past transactions as fraud or fair transactions. This forms the class attribute.

Learn a model for the class of the transactions.

Use this model to detect fraud by observing credit card transactions on an account.

From: http://www-users.cs.umn.edu/~kumar/dmbook/index.php

Slide39

Customer Attrition/Churn:Goal: To predict whether a customer is likely to be lost to a competitor.

Approach:

Use detailed record of transactions with each of the past and present customers, to find attributes.

How often the customer calls, where he calls, what time-of-the day he calls most, his financial status, marital status, etc. Label the customers as loyal or disloyal.

Find a model for loyalty.

From [Berry & Linoff] Data Mining Techniques, 1997

Slide40

Helps users understand the natural grouping or structure in a data set.Cluster

: a collection of data objects that are “similar” to one another and thus can be treated collectively as one group.

Clustering:

unsupervised classification: no predefined classes

Clustering

is a process of partitioning a set of data

(or objects) in a set of meaningful sub-classes, called

clusters

.

Slide41

Find “natural” grouping of instances given un-labeled data

Slide42

A good clustering method will produce high quality clusters in which:the

intra-class

similarity (that is within a cluster) is high.

the inter-class similarity (that is between clusters) is low.The quality of a clustering result also depends on both the similarity measure used by the method and its implementation.

The

quality

of a clustering method is also measured by its ability to discover some or all of the

hidden

patterns.

The quality of a clustering result also depends on the definition and representation of cluster chosen.

Slide43

Partitioning algorithms: Construct various partitions and then evaluate them by some criterion.

Hierarchy algorithms

: Create a hierarchical decomposition of the set of data (or objects) using some criterion. There is an agglomerative approach and a divisive approach.

Slide44

Partitioning method: Given a number k

, partition a database

D

of n objects into a set of k clusters so that a chosen objective function is minimized (e.g., sum of distances to the center of the clusters).Global optimum: exhaustively enumerate all partitions – too expensive!

Heuristic methods based on iterative refinement of an initial partition

Slide45

Hierarchical decomposition of the data set (with respect to a given similarity measure) into a set of nested clustersResult represented by a so called

dendrogram

Nodes in the dendrogram represent possible clusters

can be constructed bottom-up (agglomerative approach) or top down (divisive approach)

Clustering obtained by cutting the dendrogram at a desired level: each

connected

component forms a cluster.

Slide46

cluster similarity = similarity of two most similar members

-

Potentially long and skinny clusters

+

Fast

Slide47

1

2

3

4

5

Slide48

1

2

3

4

5

Slide49

1

2

3

4

5

Slide50

cluster similarity = similarity of two least similar members

+

tight clusters

-

slow

Slide51

1

2

3

4

5

Slide52

1

2

3

4

5

Slide53

1

2

3

4

5

Slide54

Clustering obtained by cutting the dendrogram at a desired level: each connected component forms a cluster.

Dendogram: Hierarchical Clustering

Slide55

Understanding the DataData Cleaning

Missing Values, Noisy Values, Outliers

Dates

Nominal/NumericDiscretizationNormalization

Smoothing

Transformation

Attribute selection

Slide56

Can't be expected to be expert in all fields, but understanding the data can be extremely useful for data mining.What data is available?

What available data is actually relevant or useful?

Can the data be enriched from other sources?

Are there historical datasets available?Who is the Real expert to ask questions of?

(Are the results at all sensible? Or are they completely obvious?)

Answers to these questions before embarking on a data mining project are invaluable later on.

Slide57

Number of instances available:5000 or more for reliable results

Number of attributes:

Depends on data set, but any attribute less than 10 instances is typically not worth including

Number of instances per class:More than 100 per classIf very unbalanced, consider stratified sampling

Slide58

Goal: maximizing data qualityAssess data quality

Correct noted deficiencies

Assess data quality

Analyze data distribution: is there any strange data distribution? Analyze data elements: check inconsistencies, redundant, missing values, outlier, etc.Conduct physical audit: ensure data recorded properly, for example: cross check data to customer

Analyze business rules: check data violates business rules

Slide59

Exclude the attribute for which data is frequently missingExclude records that have missing data

Extrapolate missing values from other known values

Use a predictive model to determine a value

For quantitative values, use a generic figure, such as the average.

Slide60

We want all dates to be the same.  YYYY­MM­DD is an ISO standard, BUT it has some issues for data mining.Year 10,000 AD! (we only have 4 digits)

Dates BC[E] eg -0300-02-03 is not a valid YYYY-MM-DD date.

Most importantly: Does not preserve intervals, with/without the –second

Other representations:Posix/Unix System Date:  Number of seconds since 1970

etc

Slide61

Nominal data – without ordering, eg: Sex, Country, etcSome algorithms can't deal with nominal or numeric attributes. Eg Decision trees deal best with nominal, but Neural Networks and many clustering algorithms require only numeric attribute values.

In case the algorithm requires converting Nominal to Numeric

Binary field: One value is 0, other value is 1 (eg gender)

Ordered fields: Convert to numbers to preserve order (eg A vs C grade becomes 4 and 2 respectively)Few Values: Convert each value into a new binary attribute, for example: possible values for attribute AT are A, B, C, D then you can create 4 new attributes ATa, ATb, ATc, ATd with each attribute has value either 0 or 1

Many Values: Convert into groups of values, each with its own (binary) attribute. eg group states in the US into 5 groups of 10.

Unique Values: Ignore identifier like attributes (buang atribut)

Slide62

Some algorithms require nominal or discrete values. How can we turn a numeric value into a nominal value, or a numeric value with a smaller range of values.Several Discretization techniques. Often called 'binning‘.

Equal Width

Equal Depth

Class DependentEntropyFuzzy (Allow some fuzziness as to the edges of the bin)‏

Non-disjoint (Allow overlapping intervals)‏

ChiMerge (Use Chi-Squared Test in the same way as Entropy)‏

Iterative (Use some technique, then minimise classifier error)‏

Lazy (Only discretize during classification (VERY lazy!))‏

Proportional k-Interval (Number of bins = square root of instances)‏

Slide63

We might want to normalise our data such that two numeric values are comparable. For example to compare age and income.

Decimal Scaling: v' = v/10k for smallest k such that max(abs(v'))<1

Eg: -991 and 99, k is 3, and -991 becomes -0.991

Min/Max Normalisation: v'= (v-min)/(max-min) * (newMax-newMin) + newMin

Eg: 73600 in [12000,98000] to [0,1] is 0.716

Zero-mean Normalization: v'=(v-mean)/stddev

eg: mean 54000, stddev = 16000, v=73600 then v' = 1.225

Slide64

In the case of noisy data, we might want to smooth the data such that it is more uniform.

Some possible techniques:

Regression: Find the function for the data, and move each value some amount closer to what the function predicts (see classification)

Clustering: Some clustering techniques remove outliers. We could cluster the data to remove these noisy values.Binning: We could use some technique to discretize the data, and then smooth based on those 'bins'.

Slide65

Transform data to more meaningful formFor example:

Birth date

is transformed to

AgeDate of the first transaction is transformed to number of days since the customer becomes member

Grades of each course

are transformed to

cumulative GPA

Slide66

Before getting to the data mining, we may want to either remove instances or select only a portion of the complete data set to work with.Why? Perhaps our algorithms don't scale well to the amount of data we have

Techniques:

Records selection

Partitioning: Split the database into sections and work with each in turn. Often not appropriate unless the algorithm is designed to do it.Sampling: Select a random subset of the data and use that which is hopefully representative.

Attribute selection

Stepwise Forward Selection: Find the best attribute and add.

Stepwise Backward Elimination: Find the worst attribute and remove.

Genetic Algorithms: Use a 'survival of the fittest' along with random cross-breeding approach

etc

Slide67

What technique will you use to solve this problem?Given set of applicant attributes (name, salary, age, etc), you want to decide whether you have to approve customer application on credit card or not.

Given national examination scores, you want to group Kabupatens into three educational level: Good, Average, Poor

You want to suggest to your customer about suitable pant given her/his choice of shirt.

You want to estimate economic growth of Indonesia given some data (GNP, GDP, etc)

Slide68

Pak Bedu adalah seorang dokter yang ingin menggunakan TI untuk membantunya memutuskan apakah pasiennya terkena kanker atau tidak. Untuk memutuskan hal tersebut, Pak Bedu sudah memiliki data setiap pasien yang meliputi hasil uji dalam 5 test laboratorium serta keputusan apakah dia terkena kanker atau tidak. Berikut ini contoh datanya:

ID

T1

T2

T3

T4

T5

Cancer?

P1

1

3

4

2

3

Yes

P2

0

5

1

1

No

P3

1

2

4

2

2

No

P4

2

2

3

1

2

Yes

P5

2

2

3

1

2

No

Masalah apa yang bisa Anda temukan

di data Pak Bedu?

Slide69

Pada saat Pak Bedu memeriksa satu atribut (misal T1) ditemukan 5 atribut yang distinct dengan jumlah sebagai berikut:

1 dengan jumlah 1234

2 dengan jumlah 2037

3 dengan jumlah 16594 dengan jumlah 190111 dengan jumlah 1

Apa yang bisa Anda simpulkan dengan melihat data tersebut?

Slide70

Pak Bedu mengembangkan usaha kliniknya di 3 tempat. Pak Bedu menyerahkan sepenuhnya pengelolaan data pasien ke setiap klinik. Pak Bedu ingin mengetahui karakteristik dari pasien untuk usahanya dengan mengumpulkan data dari ketiga kliniknya. Hanya saja Pak Bedu bingung karena setiap klinik memiliki skema data yang berbeda-beda. Apa yang harus Pak Bedu lakukan?

Skema Klinik 1: Pasien( Nama, TglLahir, Tinggi (meter), Berat(kg), JenisKelamin (L/P), Alamat, Provinsi)

Skema Klinik 2: Pasien( Nama, TglLahir, Tinggi (centimeter), Berat(kg), JenisKelamin (P/W), Alamat, Kota )

Skema Klinik 3: Pasien( Nama, Umur, Tinggi (meter), Berat(kg), JenisKelamin (L/P), Kota, Provinsi)

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Pak Bedu ternyata juga memiliki usaha Sekolah Tinggi Kesehatan. Sebagai orang yang baik hati, Pak Bedu ingin memberikan beasiswa untuk mahasiswanya yang sedang mengerjakan skripsi. Hanya saja Pak Bedu ingin memperoleh mahasiswa yang memiliki potensi untuk bisa menyelesaikan skripsinya dalam waktu satu semester. Pak Bedu memiliki data nilai mahasiswa yang sudah lulus beserta lama waktu penyelesaian studinya. Skema data yang dimiliki Pak Bedu antara lain: Tabel Mahasiswa(NPM, Nama, Umur, AsalDaerah, LamaSkripsi, LamaStudi), Tabel MataKuliah(Kode, Nama, Kelompok), Tabel Nilai(NPM, KodeMK, Nilai)

Diskusikan apa yang kira-kira bisa Anda lakukan untuk membantu Pak Bedu

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Pak Bedu mengembangkan kliniknya sampai 100 cabang. Pak Bedu ingin melihat pola-pola kunjungan pasien, daerah mana yang banyak orang sakit, kapan banyak yang sakit, dsb. Pak Bedu memiliki data tentang klinik dan jumlah total kunjungan pasien setiap bulannya.

Apa yang bisa Anda lakukan?