mining patterns with attribute oriented induction

Mining Patterns with Attribute Oriented Induction

Spits Warnars

Database, Datawarehouse & Data Mining Research Center, Surya University

Jl. Boulevard Gading Serpong Blok O/1, Tangerang 15810, Indonesia

[email protected]

ABSTRACT

Mining data in human activity life such as business,

education, engineering, health and so on, is

important and help human itself in order to justify

their decision making process. Attribute Oriented

Induction (AOI) has been using to mine significant

different patterns since was coined in 1989, has

been combined and as complement with other data

mining pattern. AOI has been proved and powerful,

has future opportunity to be explored in order to

help human life to find data patterns. AOI is chosen

since can reduce many patterns by summarize/roll

up many patterns in low into high level in concept

tree/hierarchy. However, non summarize pattern at

low level in concept tree/hierarchy can be used to

sharpen the mining knowledge pattern just as like

roll up and drill down in data warehouse. Mapping

implementation of AOI in human life area such as

business, education, engineering, health and so on,

is useful in order to give valuable knowledge AOI

mining pattern, particularly for those who interest

with AOI data mining technique as data mining

technique which can summarize many pattern into

simple patterns.

KEYWORDS

Data Mining, Attribute Oriented Induction, AOI,

pattern, rule.

1. INTRODUCTION

Attribute Oriented Induction (AOI) method was

first proposed in 1989 integrates a machine

learning paradigm especially learning-from-

examples techniques with database operations,

extracts generalized rules from an interesting

set of data and discovers high level data

regularities [39]. AOI provides an efficient and

effective mechanism for discovering various

kinds of knowledge rules from datasets or

databases.

AOI approach is developed for learning

different kinds of knowledge rules such as

characteristic rules, discrimination rules,

classification rules, data evolution regularities

[1], association rules and cluster description

rules[2].

1) Characteristic rule is an assertion which

characterizes the concepts which satisfied

by all of the data stored in database. This

rule provide generalized concepts about a

property which can help people recognize

the common features of the data in a class.

For example the symptom of the specific

disease [9].

2) Discriminant rule is an assertion which

discriminates the concepts of one (target)

class from another (constrasting). This rule

give a discriminant criterion which can be

used to predict the class membership of of

new data,for example to distinguish one

disease from the other [9].

3) Classification rule is a set of rules which

classifies the set of relevant data according

to one or more specific attributes. For

example, classifying diseases into classes

and provide the symptoms of each [40].

4) Association rule is association relationships

among the set of relevant data. For

example, discovering a set of symptoms

frequently occurring together[12,35].

5) Data evolution regularities rule is a general

evolution behaviour of a set of the relevant

data (valid only in time-related/temporal

data). For example, describing the major

factors that influence the fluctuations of

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Proceedings of the International Conference on Database, Data Warehouse, Data Mining and Big Data (DDDMBD2015), Jakarta, Indonesia 2015

stock values through time [3,37]. Data

evolution regularities can then be classified

into characteristic rule and discrimination

rule[3].

6) Cluster description rule is used to cluster

data according to data semantics [12], for

example clustering the university student

based on different attribute(s).

2. QUANTITATIVE AND QUALITATIVE

RULES IN AOI

Rules in AOI can be represented with

quantitative and qualitative rules:

1) Quantitative rule is a rule which is

associated with quantitative information

such as statistical information which asses

the representativeness of the rule in the

database [1]. There are three types

quantitative rule i.e. quantitative

characteristic rule, quantitative

discriminative rule and quantitative

characteristic and discriminative rule.

a. Quantitative characteristic rule is

quantitative information of a

characteristic rule and each rule in final

generalization can be measured with t-

weight in formula 1.

t-weight =Votes(qa)/ (1)

where :

t-weight = percentage of each rule in

the final generalized

relation.

Votes(qa) = number of tuples in each

rule in the final

generalized relation

Where Votes(qa) is in

Votes{q1,...,qN}.

N = number of rules in the final

generalized relation.

Quantitative characteristic rule is

represented with symbol and should

be in the form of:

V(x)=target_class(x)condition1(x)

[t:w1] V...V conditionn(x)[t:wn]

Where :

x is the target class between 1..n.

n is the number of rules in the final


[t:w1] is t-weight (formula 1) for rule

1 until

[t:wn] as t-weight (formula 1) for rule

n.

Example:

V(x) = graduate(x)(Birthplace(x) Є

Canada Λ GPA(x) Є excellent)

[t:75%] V (Major(x) Є science Λ

Birthplace(x) Є Foreign Λ

GPA(x) Є good) [t:25%]

b. Quantitative discriminative rule is a

discrimination rule that use quantitative

information. Each rule in the target class

will be discriminated against a rule in

the constrating class and is measured

with d-weight in formula 2.

d-weight =Votes(qa ϵ Cj) /

(2)

where :

d-weight = percentage ratio per rule

in the target class to the

total number of tuples in

the target class and the

contrasting class for the

same rule.

Votes(qa) = number of tuples in each

rule in the target class Cj.

Cj is in {C1,...,CK}.

K = total number of the target and

constrating classess for the

same rule.

Quantitative discriminative rule is

shown with symbol and should be in

the form of:

V(x)=target_class(x) condition1(x)

[d:w1] V...V conditionn(x)[d:wn]

Where:

x is the target class between 1..n.

n is the number of rules in the target

class.

[d:w1] is d-weight (formula 2) for

rule 1 in the target class.

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[t:wn] is d-weight (formula 2) for rule

n of target class.

Example:

V(x) = graduate(x) (Birthplace(x)

ЄForeign Λ GPA(x) Є good)

[d:100%] V (Major(x) Є social

Λ GPA(x) Є good) [d:25%]

c. Quantitative characteristic and

discriminative rule use quantitative

information characteristic rule and

discriminative rule which have both t-

weight and d-weight for the same rules.

Each rule is measured with t-weight in

formula 1 for characteristic rule and d-

weight in formula 2 for discriminative

rule. Quantitative characteristic and

discriminative rule is shown with

symbol and should be in the form

of:

V(x)=target_class(x)

condition1(x)[t: w1,d:w1] V...V

conditionn(x)[t:wn,d:wn]

Where:

x is target class between 1..n.

n is number of rules in target class.

[t: w1] is t-weight in formula 1.

[d: w1] is d-weight in formula 2.

Example:

V(x) = professor(x) (Birthplace(x)

ЄForeign Λ GPA(x) Єgood)

[t:20%,d:100%] V (Major(x) Є

social Λ GPA(x) Є good)

[t:10%,d:25%]

2) Qualitative rule can be obtained by using

the same process of learning applied in its

quantitative counterpart without the

association of the quantitative attribute in

the generalized relations [1]. Qualitative

characteristic rule uses symbol and

qualitative discriminative rule uses

symbol. Qualitative rule either

characteristic or discriminative rules should

be in the form of:

V(x)=target_class(x) [|] condition1(x)

V...V conditionn(x)

Example:

V(x) = graduate(x) (Birthplace(x)

ЄCanada Λ GPA(x) Є excellent) V

(Major(x) Є science Λ Birthplace(x)

Є Foreign Λ GPA(x) Є good)

3. Concept Hierarchies

One advantage of AOI is that it has concept

hierarchy as the background knowledge which

can be provided by the knowledge engineers or

domain experts [2,3,4]. Concept hierarchy

stored a relation in the database provides

essential background knowledge for data

generalization and multiple level data mining.

Concept hierarchy represents a taxonomy of

concept of the attribute domain values. Concept

hierarchy can be specified based on the

relationship among database attributes or by set

groupings and be stored in the form of relations

in the same database [7].

Concept hierarchy can be adjusted dynamically

based on the distribution of the set of data

relevant to the data mining tasks. The

hierarchies for numerical attributes can be

constructed automatically based on data

distribution analysis [7]. Concept hierarchy for

numeric will be treated differently for the sake

of efficiency [20,21,22,23,26]. For example if

there are a range of value between 0 and 1.99,

then there will be 199 values start from 0.00

until 1.99, but for efficiency there will be only

1 record created with 3 fields rather than with

200 records with 2 fields.

In concept hierarchy concepts are ordered by

levels from specific or low level concepts into

general or higher level. Generalization is

achieved by ascending to the next higher level

concepts along the paths of the concept

hierarchy. The most general concept is the null

description as the most specific concepts

correspond to the specific values of the

attributes in the database which described as

ANY. Concept hierarchy can be balanced or

unbalanced, where unbalanced hierarchy then

must be converted to a balanced hierarchy.

Figure 1 shows the concept hierarchy tree for

attribute workclass in adult dataset [18] which

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has three levels. The first level as the low level

has 8 concepts and they are without-pay, never-

worked, private, self-emp-not-inc, self-emp-inc,

federal-gov,state-gov and local-gov concepts.

The second level has 5 concepts and they are

charity, unemployed, entrepreneur, centre and

territory concepts. The third level as the high

level has 2 concepts and they are non

government and government concepts. For

example, the concept of non government at the

high level has 3 sub concepts in the second

level: charity, unemployed and entrepreneur

concepts. The concept entrepreneur at the

second level has 3 sub concepts in the low

level: private, self-emp-not-inc and self-emp-

inc concepts.

Figure 1. A concept hierarchy tree for attribute

workclass in adult dataset[18]

Concept hierarchy in figure 1 can be

represented with:

Without-pay Charity

Never-worked Unemployed

{Private, self-emp-not-inc,

self-emp-inc} entrepreneur

{federal-gov,state-gov} Centre

Local-gov Territory

{Charity,Unemployed,

entrepreneur} Non government

{Centre, Territory} Government

{Non government,

Government} ANY(workclass)

Where symbol indicates generalization, for

example Without-pay Charity indicates that

Charity concept is a generalization of Without-

pay concept.

There are four types of concept generalization

in the concept hierarchy [6]:

1) Unconditional concept generalization: rule

is associated with the unconditional IS-A

type rules. A concept is generalized to a

higher level concept because of the

subsumption relationship indicated in the

concept hierarchy.

2) Conditional/deductive rule generalization:

rule is associated with a generalization path

as a deduction rule where the type of rules

is conditional and can only be applied to

generalize a concept if the corresponding

condition can be satisfied. For example,

form: A(x) Λ B(x) C(x) has the meaning

that for a tuple x, the concept(attribute

value) A can be generalized to concept C if

condition B can be satisfied by x. Or

concept C can be generalized if it can be

satisfied by concept A and B.

3) Computational rule generalization: each

rule is represented by a condition which is

value-based and can be evaluated against an

attribute or a tuple or the database by

performing some computations. The true

value of the condition would then determine

whether a concept can be generalized via

the path.

4) Hybrid rule-based concept generalization: a

hierarchy can have paths associated with all

the above 3 different types of rules. It has a

powerful representation capability and is

suitable for many kinds of application.

Rules number 2-4 is three types of rule based

concept hierarchy [5,34] while rule number 1 is

a non rule based concept hierarchy.

A rule-based concept hierarchy is a concept

hierarchy whose paths have associated

generalization rules. In the rule-based

induction, data cube (hypercube) in

multidimensional datawarehouse is the

favourable data structure [6]. To perform a rule-

based induction on the data in a large

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warehouses, the path relation algorithm is an

excellent choice because datawarehouse has

already structured as cube/hypercube [6]. Rule-

based concept has induction anomaly problem

which affects the efficiency which is caused by:

1) A rule may depend on an attribute which

has been removed.

2) A rule may depend on an attribute whose

concept level in the prime relation has been

generalized too high to match the condition

of the rule.

3) A rule may depend on a condition which

can only be evaluated against the initial

relation, e.g. the number-of-tuples in the

relation.

There are three ways to solve the induction

anomaly problem [6]:

1) Reapplying the deduction rules all over

again on the initial relation which are costly

and wasteful.

2) Repetitive generalization required by roll-

up and drill-down which can be done in an

efficient way without induction anomaly

problem.

3) Propose the use of path relation (the last

method backtracking algorithm [5,6]

4. AOI prototype

The AOI method was implemented in a data

mining system prototype called DBMINER

[5,7,17,28,29] which previously called

DBLearn and been tested successfully against

large relational database. DBLearn

[24,25,27,38] is a prototype data mining system

which was developed in Simon Fraser

University. DBMINER was developed by

integrating database, OLAP and data mining

technologies [17,36] has following features:

1) Incorporating several data mining

techniques like attribute oriented induction,

statistical analysis, progressive deepening

for mining multiple-level rules and meta-

rule guided knowledge mining [7] data cube

and OLAP technology [17].

2) Mining new kinds of rules from large

databases including multiple level

association rules, classification rules, cluster

description rules and prediction.

3) Automatic generation of numeric

hierarchies and refinement of concept

hierarchies.

4) High level SQL-like and graphical data

mining interfaces.

5) Client server architecture and performance

improvements for larger application.

6) SQL-like data mining query language

DMQL and Graphical user interfaces have

been enhanced for interactive knowledge

mining.

7) Perform roll-up and drill-down at multiple

concept levels with multiple dimensional

data cubes.

5. AOI algorithms

AOI can be implemented with an architecture

design shown in figure 2 where characteristic

rule (LCHR) and classification rule (LCLR) can

be learned directly from the transactional

database (OLTP) or Data warehouse (OLAP)

[6,8] with the help of the concept hierarchy as

the knowledge generalization. Concept

hierarchy can be created from OLTP database

as a direct resource.

Figure 2. AOI architecture

From a database we can identify two types of

learnings:

1) Positive learning as the target class where

the data are tuples in the database which are

consistent with the learning concepts.

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Positive learning/target class will be built

when learn characteristic rule

2) Negative learning as the contrasting class in

which the data do not belong to the target

class. negative learning/contrasting class

will be built when learn discrimination or

classification rule.

Characteristic rule has been used by AOI in

order to recognize, learning and mining as a

specific character for each of attribute as their

specific mining characterization. Characteristic

rule process the generalization with help of

concept hierarchy as the standard saving

background knowledge to find target class as a

positive learning. Mining rule can not be

limited with just only one rule, as the more

rules can be created the more mining can be

done. This has been proven as an intelligent

system which can help human to make a system

that has ability to think like a human [3]. Rules

often can be discovered by generalization in

several possible directions [9].

Relational database as resources for data

mining with AOI can be read with data

manipulation language select sql statement

[13,14,15,16]. Using a query for building rules

gives an efficient mechanism for understanding

the mined rules [11,12]. In the current AOI, a

query is processed with SQL-like data mining

query language DMQL at the beginning of the

process. It collects the relevant sets of data by

processing a transformed relational query,

generalizes the data by AOI and then presents

the outputs in different forms [7].

AOI generalizes and reduces the prime relation

further until the final relation can satisfy the

user expectation based on the set threshold. One

or two thresholds can be applied, where one

threshold is used to control both of number of

distinct attributes and tuples in the

generalization process, whilst two thresholds

are used to control the number of distinct

attributes and tuples in the generalization

process.

Threshold as a control for the maximum

number of tuples of the target class in the final

generalized relation can be replaced with group

by operator in sql select statement which will

limit the final result of generalization. Setting

different threshold will generate different

generalized tuples as the needed of global

picture of induction repeatedly as time-

consuming and tedious work [10]. All

interesting generalized tuples as multiple rule

can be generated as the global picture of

induction by using group by operator or distinct

function in the sql select statement.

AOI can perform datawarehouse techniques by

doing generalization process repetitively in

order to generate rules at different concepts

levels in a concept hierarchy, enabling the user

to find the most suitable discovery levels and

rules. This technique performs roll up

(progressive generalization [6]) or drill down

(progressive specialization [6]) and operation

[2,7] have been recognized as datawarehouse

techniques. Finding the most suitable discovery

levels and rules would add multidimensional

views to a database using generalization

process repetitively at different concepts level.

Building a logical formula as the representation

of a final result of AOI can not be done with

select sql statement and not select sql statement.

However, the sql statement can be matched

with other applications like Java, Visual Basic,

programming server program like ASP, JSP or

PHP. The data resulted from the sql statement

can be used to create a logical formula using

one of those application softwares.

There are 8 strategy steps that must be done [3]

in the process of generalization. Here is step

one to seven which is for characteristic rule and

step one to eight are for

classification/discriminant rule.

1) Generalization on the smallest

decomposable components, generalization

should be performed on the smallest

decomposable components of a data

relation.

2) Attribute removal, if there is a large set of

distinct values for an attribute but there is

no higher level concept provided for the

attribute, the attribute should be removed

during generalization.

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3) Concept tree Ascension, if there exists a

higher level concept in the concept

hierarchy for an attribute value of a tuple,

the substitution of the value by its higher

level concept would generalize the tuples.

4) Vote propagation, the value of the vote is

the value of accumulated tuples where the

vote will be accumulated when merging

identical tuples in the generalization.

5) Threshold control on each attribute, if the

number of distinct values in a resulting

relationthe is larger than the specified

threshold value, further generalization on

this attribute should be performed.

6) Threshold control on generalized relations,

if the number of tuples is larger than the

specified threshold value, further

generalization will be done based on the

selected attributes and the merging of the

identical tuples should be performed.

7) Rule transformation, change final

generalization to quantitative rule and

qualitative rule from a tuple (conjunctive)

or multiple tuples (disjunctive).

8) Handling overlapping tuples, if there are

overlapping tuples in both target and

constrasting classes, these tuples should be

marked and eliminated from the final


AOI characteristic rule algorithm [3] is given as

follow:

For each of attribute Ai (1 i n, where n= # of attributes) in the

generalized relation GR

{ While

#_of_distinct_values_in_attribute_Ai >

threshold

{If no higher level concept in

concept hierarchy for

attribute_Ai

Then remove attribute Ai

Else substitute the value of Ai

by its corresponding

minimal generalized

concept

Merge identical tuples

}

}

While #_of_tuples in GR > threshold

{ Selective generalize attributes


}

This AOI characteristic rule algorithm is the

implementation of step one to seven of the

generalization strategy steps. The algorithm

shows two sub processes i.e. control number of

distinct attributes and control number of tuples.

1) Control number of distinct attributes is a

vertical process which checks every per

attribute vertically. This is done by

checking all attributes in the learning results

of a dataset which have distinct attributes

less equal than the threshold. This first sub

process is just applied attributes that have

distinct attributes greater than threshold

while the number of distinct attributes are

also greater than the threshold. Each

attribute which have distinct attribute

greater than threshold will be checked if it

has a higher level concept in the concept

hierarchy. If it has no higher level concept

then the attribute will not be used. On the

other hand if they have higher level concept

then the attribute value will be substituted

with the value of the higher level concept.

Merging identical tuples will be done in

order to summarize generalization and

accumulate the value of the vote of the

identical tuples by eliminating the

redundant tuples. Eventually, after this first

sub process all the attributes in

generalization will have number of distinct

attributes less equal than the threshold. This

first sub process is implementation of step

one to five of the generalization strategy

steps.

2) Control number of tuples is a horizontal

process which checks per rule horizontally.

This is carried out for those attributes which

passed the first sub process where each

attribute will have number of distinct

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attributes less equal than the threshold. This

second sub process is only done while the

number of rules is greater than threshold.

Selective generalization of the attributes

and merging of the indentical tuples will

reduce the number of rules. Selecting

candidate attribute for further generalization

can be done by preferences with finding the

ratio on the number of tuples or the number

of distinct attribute values. Selecting

candidate attribute for further generalization

can be examined by user based on the non

interesting one, either non interesting

attribute or non interesting rule. As with

first sub process merging the identical

tuples will be done in order to summarize

generalization and accumulate the vote

value of identical tuples by eliminating the

redundant tuples. Eventually, after this

second sub process the number of rules is

less equal than the threshold. This second

sub process is the implementation of step

three, four and six of the generalization

strategy steps.

AOI discriminant rule algorithm [1] is shown

below:

For each of attribute Ai (1 i n, where n= # of attributes) in the

generalized relation GR

{ Mark the overlapping tuples

While

#_of_distinct_values_in_attribute_Ai >

threshold

{ If no higher level concept in

concept hierarchy for

attribute_Ai

Then remove attribute Ai

Else substitute the value of

Ai by its corresponding

minimal generalized

concept

Mark the overlapping tuples


}

}

While #_of_tuples in GR > threshold

{ Selective generalize attributes

Mark the overlapping tuples


}

AOI discriminant rule algorithm is the

implementation of step one until eight of

generalization strategy steps. Since AOI

discriminant rule and AOI characteristic rule

algorithms have the same generalization

strategy steps between steps one and seven,

then literally they have the same process and

the difference is just only in step eight. They

also have the same sub processes i.e. control

number of distinct attributes as the first sub

process and control number of tuples as the

second sub process. The step handling

overlapping tuples as the eight generalization

strategy step is process in the beginning before

the first sub process and both in first and

second processes before merge indentical

tuples.

6. AOI Advantages and disadvantages

AOI provides a simple and efficient way to

learn knowledge rules from a large database

and has many advantages [9] such as:

1) AOI provides additional flexibility over

many machine learning algorithms.

2) AOI can learn knowledge rules in different

conjunctive and disjunctive forms and

provides more choices for the experts and

users.

3) AOI can use database facilities as the

traditional relational database such as

selection, join, projection whereas most

learning algorithms suffer from inefficiency

problems in a large database environment.

4) AOI can learn qualitative rules with

quantitative information while many

machine learning algorithm only can learn

qualitative rules.

5) AOI can handle noisy data and exceptional

cases elegantly by incorporating statistical

techniques in the learning process whereas

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some learning system can only work in a

‘noise free’ environment.

However, AOI also has disadvantages [10] such

as:

1) AOI can only provides a snapshot of the

generalized knowledge and not a global

picture. Yet, the global picture can be

revealed by trying different thresholds

repeatedly.

2) Adjusting different thresholds will result in

different sets of generalized tuples.

However, using different thresholds

repeatedly is a time consuming and tedious

work.

3) There will be a problem in selecting the best

generalized rules between the large and

small threshold. Where in a large threshold

value will lead to a relatively complex rule

with many disjuncts and the results may not

be fully generalized. On the other hand a

small threshold value will lead to a simple

rule with few disjuncts and the results may

over generalized the rule with a risk of

losing some valuable informations.

7. AOI Current Studies

There are a number of recent studies on AOI. One study by Chen et al has proposed a global AOI method employing multiple-level mining technique with multiple minimum supports in order to generalize all interesting general knowledge [30]. Wu et al have proposed a Global Negative AOI (GNAOI) approach that can generate comprehensive and multiple-level negative generalized knowledge at the same time [31]. Furthermore, Muyeba et al have proposed clusterAOI, a hybrid interestingness heuristic algorithm, which uses attribute features such as concept hierarchies and distinct domain attribute values to dynamically recalculate new attribute thresholds for each less significant attribute [32]. Moreover, Huang et al have introduced the Modified AOI (MAOI) method to deal with the multi-valued attribute table and further sort the readers into different

clusters. Instead of using the concept hierarchy and concept trees, MAOI method implemented the concept climbing and generalization of multi-valued attribute table with Boolean Algebra and modified Karnaugh Map, and then described the clusters with concept description [33].

Meanwhile, Over generalization problem in AOI was reduced with entropy measurement, where AOI algorithm was extended by feature selection for generalization process depends on feature entropy measurement [41]. Meanwhile, AOI is combined with EP(Emerging Pattern) become AOI-HEP(Attribute Oriented Induction High Emerging Pattern) use to mine frequent and similar pattern [42,43,44] and have future research such as inverse discovery learning, learning more than two datasets and learning other knowledge rules[45]. Moreover, MAOI (Modified AOI) algorithm was proposed to deal with multi-valued attributes which convert the data to Boolean bit uses K-map to converge the attributes[46]. Furthermore, AOI was modified and called Frequency Count AOI (FC-AOI) and used to mine the network data[47]. Meanwhile, AOI was extended and used as Extended Attribute Oriented Induction (EAOI) for clustering mixed data type, where EAOI has function to drawback major values and numeric attributes[48,49].

Moreover, AOI was chosen as second step from 5 steps proposed algorithm in order to produce AOI characteristic rule for parallel machine scheduling[50]. Another approach was proposed where doing classification using decision tree induction which improve C4.5 classifier with 4 steps where the first step is generalization by AOI[51]. Meanwhile, CFAOI (Concept-Free AOI) was proposed in order to improve AOI from the constraint of concept tree on multi value attributes, by combining the simplified binary digits with Karnaugh Map [52].

8. Conclusion

AOI has ages of 26 years since 1989 proof that

still exist in finding pattern and have been

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combined and as complement with other data

mining techniques. AOI can mine many

different patterns and other possible patterns in

the future. AOI has been proof as powerful

mining technique when many patterns can be

mining with simple pattern results. AOI has

powerful in order to roll up/summarize data in

low to high level in concept tree/hierarchy,

which show that produce simple pattern.

Implementation AOI shows that AOI is useful

and recognized to mine pattern summarize

pattern from huge pattern and many kinds of

different patterns. Using AOI in many kind of

field such as business, education, engineering,

health and so on, should be mapped in order to

increase the reliability of AOI as proof and

powerful data mining technique.

Acknowledgement

This research is supported under Program of

research incentive of national innovation

system (SINAS) from Ministry of Research,

Technology and Higher Education of the

Republic of Indonesia, decree number

147/M/Kp/IV/2015, Research code: RD-2015-

0020.

REFERENCES

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