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Mining Association Rules Using

Population Based Stochastic Search

Algorithms

K.IndiraResearch Scholar / CSE

Under the Supervision ofDr.S.Kanmani

Professor / Dept. of IT,

Pondicherr En!ineerin! Colle!e,

Puducherr.

PU"#IC $I$% $&CEE'%(I)%TI&)

*+.*+.*

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ORGANIZATION

OBJECTIVEINTRODUCTION

MOTIVATION

RESEARCH PROPOSAL

EMPIRICAL STUDY

CONCLUSION

PUBLICATIONS

REFERENCES

-

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OBJECTIVE

To develop effiie!" #e"$odolo%& fo' #i!i!% A((oi)"io!

'*le( *(i!% pop*l)"io! +)(ed (e)'$ #e"$od( !)#el&

Ge!e"i Al%o'i"$# ,GA-

P)'"ile S.)'# Op"i#i/)"io! ,PSO-

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DATA MINING

E0"')"io! of i!"e'e("i!% i!fo'#)"io! o'

p)""e'!( f'o# d)") i! l)'%e d)")+)(e( i( 1!o.!

)( d)") #i!i!%2

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ASSOCIATION RULE MINING

A((oi)"io! '*le #i!i!% fi!d( i!"e'e("i!%

)((oi)"io!( )!d3o' o''el)"io! 'el)"io!($ip()#o!% l)'%e (e" of d)") i"e#(2

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ASSOCIATION RULES

A((oi)"io! R*le( )'e of fo'# X Y .i"$ ".oo!"'ol p)')#e"e'( (*ppo'" )!d o!fide!e

S*ppo'"4 s4 p'o+)+ili"& "$)" ) "')!()"io!o!")i!( 5 Y

Co!fide!e4 c,o!di"io!)l p'o+)+ili"& "$)" )"')!()"io! $)vi!% 5 )l(o o!")i!( Y

0

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Doe( !o" fi" i! #e#o'& )!d i( e0pe!(ive "o +*ild

E5ISTINGSYSTEM

Ap'io'i4 FP G'o."$ T'ee4 6l)" )'e (o#e of "$epop*l)' )l%o'i"$#( fo' #i!i!% AR(

T')ve'(e "$e d)")+)(e #)!& "i#e(

I3O ove'$e)d4 )!d o#p*")"io!)l o#ple0i"& i(

#o'e

C)!!o" #ee" "$e 'e7*i'e#e!"( of l)'%e8()le

d)")+)(e #i!i!%

E5ISTINGSYSTEMLIMITATIONS

1

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EVOLUTIONARY COMPUTING

P'ovide 'o+*(" )!d effiie!" )pp'o)$ i! e0plo'i!%

l)'%e (e)'$ (p)e

Appli)+le i! p'o+le#( .$e'e !o ,%ood- #e"$od i(

)v)il)+le

Mo(" (*i")+le i! p'o+le#( .$e'e #*l"iple (ol*"io!(

)'e 'e7*i'ed

P)')llel i#ple#e!")"io! i( e)(ie'

T$e& )'e ("o$)("i4 pop*l)"io!8+)(ed (e)'$)l%o'i"$#(

2

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Evol*"io!)'& o#p*"i!% "e$!i7*e( #o("l& i!volve Me")$e*'i("iOp"i#i/)"io! Al%o'i"$#(2

Evolutionary algorithms

Gene expression programming

Genetic Algorithm

Genetic programmingEvolutionary programming

Evolution strategy

Differential evolution

Differential search algorithm

Eagle strategy

Swarm intelligence

Ant colony optimization

Particle Swarm Optimization

Bees algorithm

Cucoo search

3

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Evol*"io!)'& o#p*"i!% "e$!i7*e( #o("l& i!volve Me")$e*'i("iOp"i#i/)"io! Al%o'i"$#(2

Evolutionary algorithms

Gene expression programming

Genetic Algorithm

Genetic programmingEvolutionary programming

Evolution strategy

Differential evolution

Differential search algorithm

Eagle strategy

Swarm intelligence

Ant colony optimization

Particle Swarm Optimization

Bees algorithm

Cucoo search

*+

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Ge!e"i Al%o'i"$# ,GA- )!d P)'"ile S.)'#

Op"i#i/)"io! ,PSO- )'e effe"ive pop*l)"io! +)(ed("o$)("i (e)'$)l%o'i"$#(4 .$i$ i!l*de $e*'i("i(

)!d )! ele#e!" of !o!de"e'#i!i(# i! "')ve'(i!% "$e

(e)'$ (p)e2

GA AND PSO 9 AN INTRODUCTION

**

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GENETIC ALGORITHM

A Ge!e"i Al%o'i"$# ,GA- i( ) p'oed*'e *(ed "o

fi!d )pp'o0i#)"e (ol*"io!( "o (e)'$ p'o+le#(

"$'o*%$ "$e )ppli)"io! of "$e p'i!iple( of

evol*"io!)'& +iolo%&2

*-

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GENETIC ALGORITHM

P&PU#%TI&) SE#ECTI&)

(UT%TI&) CR&SS&$ER *

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PARTCILE S:ARM OPTIMIZATION

PSO( #e$)!i(# i( i!(pi'ed +& "$e (oi)l )!d

oope')"ive +e$)vio' di(pl)&ed +& v)'io*((peie( li1e +i'd(4 fi($ e" i!l*di!% $*#)!

+ei!%(2

*

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Updation of velocitof particle in each

Iteration

-Particle

4 "estparticle of thes5arm

PARTCILE S:ARM OPTIMIZATION

6eneratio

n *

6eneration -

Tar!et7Solution8

6eneration )

*

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Methodology

Modification

Parameter

Adaptation

(inin! %R5ith %daptive

PS& 7dataindependent8

(inin! %R5ith %daptive

PS& 7datadependent8

(inin!%ssociation

Rules 5ithchaotic PS&

(inin!%ssociation rules

5ith Dnamic)ei!h9orhood

Selection in PS&

%ssociation Rule 7%R8 (inin!

6enetic %l!orithm76%8

Particle S5arm&ptimi:ation 7PS&8

Com9ination(ethods

(inin!%ssociationrules 5ith

;PS&

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RESEARCH CONTRIBUTIONS

PHASE I

Parameter !uning in GA for Association "ule #iningGA with Elitism for A"#

Association "ule mining using a$aptive GA

*1

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PHASE I



PHASE II#ining Association rules %ase$ on chaotic #aps&eigh%orhoo$ selection in PSO for A"#

A$aptive PSO for A"# 'non $ata $epen$ent(

Data $epen$ent a$aptation in PSO for A"#

*2

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PHASE I






PHASE III)y%ri$ization of GA an$ PSO

#emetic PSO with Shuffle$ *rog +eaping Algorithm

,uantum Behave$ PSO for A"# *3

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PHASE I






PHASE III)y%ri$ization of GA an$ PSO

#emetic PSO with Shuffle$ *rog +eaping Algorithm

,uantum Behave$ PSO for A"# -+

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DATASETS

Le!(e(

H)+e'#)!

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DATASETS

D)")(e" No2 ofI!(")!e(

No2 ofA""'i+*"e(

A""'i+*"e$)')"e'i("i(

Le!(e( => ? C)"e%o'i)l

H)+e'#)!

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Phase * 4 6% 9ased%R(

Parameter Tunin!for %R(

6% 5ith Elitism

%daptive 6%

-

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PARAMETERS OF GA

Parameter)ame Parameter Role

Pop*l)"io! Si/e Fi0e( "$e !*#+e' of $'o#o(o#e( )!di!di'e"l& "$e 'o((ove'

Sele"io! Sele"io! of "$e $'o#o(o#e( fo''o((ove'

M*")"io! ')"e ,p#- T$e #*")"io! ope')"io! i( +)(ed o!#*")"io! ')"e

C'o((ove' ')"e ,p- T$e 'o((ove' poi!"( i( fi0ed +&'o((ove' ')"e

Mi!i#*# (*ppo'"Mi!i#*# o!fide!e

Se" +& "$e *(e' fo' fi"!e(( )l*l)"io!

-

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%R( 9 6%

Me"$odolo%&Sele"io! 9 To*'!)#e!"

C'o((ove' P'o+)+ili"& 9 @2=

M*")"io! P'o+)+ili"& 9 @2=

Fi"!e(( F*!"io! 9

Pop*l)"io! 9 Fi0ed

-

>Performance %nalsis of 6enetic %l!orithm for (inin!%ssociation Rules?, International @ournal of ComputerScience Issues, $ol. 3, Issue -, )o *, 02410, (arch -+*-

> Rule %cAuisition usin! 6enetic %l!orithm?, @ournal ofComputin!, $olume , Issue , *-24*, (a -+*-

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%R( 9 ParameterTunin!

Me"$odolo%&

Sele"io! 9 To*'!)#e!"

C'o((ove' P'o+)+ili"& 9 Te("ed .i"$ ? v)l*e(

M*")"io! P'o+)+ili"& 9 No M*")"io!

Fi"!e(( F*!"io! 9

Pop*l)"io! 9 Te("ed .i"$ ? v)l*e(

-0

Mi!i!% A((oi)"io! R*le( U(i!% GA Al%o'i"$#9 T$e 'ole of E("i#)"io!P)')#e"e'(< 4 I! 9 I!"e'!)"io!)l o!fe'e!e o! )dv)!e( i! o#p*"i!%)!d o##*!i)"io!(4 Sp'i!%e' LNCS4 Vol*#e @4 P)'" 4 ?8>4=@2

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Flo.$)'" of ARM *(i!% GA

-1

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RESULT ANALYSIS

Pop*l)"io! Si/e V( A*')& fo'ARM .i"$ GA

-2

50

55

60

65

70

75

80

85

90

No. of Instances

No. of Instances

*0.75

No. of Instances

* 1.25

Datasets

Predictive %ccurac B

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RESULT ANALYSIS

Mi!i#*# S*ppo'" )!d Co!fide!e V( A*')&fo' ARM .i"$ GA

-3

sup=0.2 con=0.210

20

30

40

50

60

70

80

90

100

Lenses

Haberan

!ar "#a$uat%on

&ostop

'oo

(inimum Support and Condence


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RESULT ANALYSIS

Pc +.- Pc +. Pc +.1

%ccur

ac B

)o. of6eneratio

ns

%ccurac

B

)o. of6eneratio

ns

%ccura

c B

)o. of6eneratio

ns

#enses 3 2 3 *0 3 *

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INFERENCES

Mi!i#*# (*ppo'"4 #i!i#*# o!fide!e deide(

*po! "$e )*')& of "$e (&("e#

C'o((ove' ')"e )ffe"( "$e o!ve'%e!e ')"e

T$e op"i#*# v)l*e of "$e GA p)')#e"e'( v)'ie(

f'o# d)")(e" "o d)")(e"

*

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6% 5ith Elitism

%daptive 6%

-

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(atin!Pool

Selection Crossover

(utation)e5

Solutions

Elitism

ElitePopulation

F')#e.o'1 fo' Co#p)'i(o! of A((oi)"io! R*le Mi!i!% U(i!% Ge!e"iAl%o'i"$#

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)o. &f

Iterati

ons

#enses Car

Evaluat

ion

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P'edi"ive A*')& fo' Mi!i!% AR +)(ed o! GA .i"$

Eli"i(#

RESULTS ANALYSIS

+

-+

+

0+

2+

*++

*-+

6%

6% 5ith Elitism

Datasets

Predictive %ccurac 7B8

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INFERENCES

Elitism 9ased 6% !ives 9etter

accurac than simple 6%

0

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6% 5ith Elitism

%daptive 6%

1

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MINING AR USING AGA

Me"$odolo%&

Sele"io! 9 Ro*le""e :$eel

C'o((ove' P'o+)+ili"& 9 Fi0ed

M*")"io! P'o+)+ili"& 9

Fi"!e(( F*!"io! 9

Pop*l)"io! 9 Fi0ed

2

R*le A7*i(i"io! i! D)") Mi!i!% U(i!% ) Self Ad)p"ive Ge!e"i Al%o'i"$#

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FLO:CHART OF AGA

()(&I+"

(aF 6enerationG

Initial Population

Evaluate =itness

Select SurvivorsCrossover

&utput Results

Yes

No

(utation

3

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RESULT ANALYSIS

A*')& Co#p)'i(o! Be".ee! GA )!d AGA

+

+

*+

-+

+

+

+

0+

1+

2+

3+

*++

6%

%6%

Datasets


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INFERENCES

A*')& o#p)'i(o! +e".ee! GA4 AGA )!d GA .i"$p)')#e"e'( (e" "o "e'#i!)"io! v)l*e( of AGA

RESULT ANALYSIS

*

Lenses &ost,p 'oo Haberan !ar "#a$uat%on0

10

20

30

40

50

60

70

80

90

100

(

((

( %t/

((

paraeter

Datasets


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INFERENCES

Ad)p"ive GA pe'fo'#( +e""e' "$)! GA i!

"e'#( of p'edi"ive A*')&

T$e Ad)p"ive P)')#e"e' Se""i!% le)d( "o

+e""e' Pe'fo'#)!e

-

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Phase - H PS& 9ased %R(

(odications inmethodolo!

Parameter Tunin!

CPS&

S%PS&

)PS&

%PS&

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(inin! %Rsusin! PS&

(ethodolo!Each data itemset are represented asparticles

The particles moves 9ased on velocit

The particles position are updated 9ased on

ei!hted PS&velocit update eAuation is modied as

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$elocit Updationin PS&

Pop*l)"io! B)(ed Se)'$ Me"$od( i! Mi!i!% A((oi)"io! R*le(< 4 I! 9T$i'dI!"e'!)"io!)l Co!fe'e!e o! Adv)!e( i! Co##*!i)"io!4 Ne".o'14 )!dCo#p*"i!%4 LNICST4 pp2 ==4 =@=2

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PSO STATES

E0plo')"io! E0ploi")"io!

Particle

"estParticle of

S5arm 0

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PSO STATES

Co!ve'%e!e J*#pi!% O*"

1

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For each particles position (p)evaluate fitness

If fitness(p) better than

fitness(pbest) then pbest pLoop

untilall

particlesexhaus

t

!et best of p"ests as g"est

#pdate particles velocity and

position

$oopun

tilma%iter

!tart

Initiali&e particles 'ith random position

and velocity vectors

!topgiving gBest* optimal solution

FLO:CHART OF PSO

2

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RESULTS ANALYSIS

Dataset )ame 6% PS&

#enses 2 3-.2

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RESULTS ANALYSIS

Fecution Time Comparison "et5een 6% and P

+

+

-+

+

0+

2+

*++

*-+

*+

6%

PS&

Datasets

EFecution Time in sec

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P'edi"ive A*')& Co#p)'i(o! +& Al"e'i!% I!e'"i):ei%$"(

RESULTS ANALYSIS

*

60

65

70

75

80

85

90

95

100

LensesHaberan

!ar "#a$uat%on

&ost,p

'oo

Inertia ei!ht


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MINING ARS USING CHAOTIC PSO

T$e !e. $)o"i #)p #odel i( fo'#*l)"ed )(

Me"$odolo%&

I!i"i)l poi!" u0and v0to 0.1T$e veloi"& of e)$ p)'"ile i( *pd)"ed +&

-

E!$)!i!% P)'"ile S.)'# op"i#i/)"io! *(i!% $)o"i ope')"o'( fo'A((oi)"io! R*le Mi!i!% 4?84 =@=2

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RESULT ANALYSIS

P'edi"ive A*')& Co#p)'i(o! of CPSO .i"$ PSO

0+

0

1+

12+

2

3+

3

*++

PS&

cPS&

Datasets


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#enses

Dataset

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RESULT ANALYSIS

Car Evaluation

Dataset

Postoperative Patient

Dataset

Co!ve'%e!e R)"e Co#p)'i(o! of CPSO .i"$ PSO

&, !&,86

88

90

92

94

96

98

100

10

20

30

40

50

60

70

80

90

100

(ethodolo!


&, !&,0

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50

60

70

80

90

100

(ethodolo!


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RESULT ANALYSIS

oo

Dataset

Co!ve'%e!e R)"e Co#p)'i(o! of CPSO .i"$ PSO

&, !&,0

10

20

30

40

50

60

70

80

90

100

10

2030

40

50

60

70

80

90

100

(ethodolo!


0

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INFERENCES

Be""e' )*')& "$)! PSO

T$e C$)o"i Ope')"o'( o*ld +e $)!%ed +&

)l"e'i!% "$e i!i"i)l v)l*e( i! $)o"i ope')"o'

f*!"io!

T$e +)l)!e +e".ee! e0plo')"io! )!d e0ploi")"io!i( #)i!")i!ed

1

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Parameter Tunin!

CPS&

S%PS&

)PS&

%PS& 2

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T$e o!ep" of lo)l +e(" p)'"ile ,l+e("- 'epl)i!% "$ep)'"ile +e(" ,p+e("- i( i!"'od*ed

T$e !ei%$+o'$ood +e(" ,l+e("- (ele"io! i( )( follo.(

C)l*l)"e "$e di(")!e of "$e *''e!" p)'"ile f'o#o"$e' p)'"ile( Fi!d "$e !e)'e(" # p)'"ile( )( "$e !ei%$+o' of "$e

*''e!" p)'"ile +)(ed o! di(")!e )l*l)"ed C$oo(e "$e lo)l op"i#*# l+e(" )#o!% "$e

!ei%$+o'$ood i! "e'#( of fi"!e(( v)l*e(

Mi!i!% AR( U(i!% NPSO

Me"$odolo%&

3

A((oi)"io! R*le Mi!i!% +& D&!)#i Nei%$+o'$ood Sele"io! i! P)'"ileS.)'# Op"i#i/)"io!

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=#&=C

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RESULT ANALYSIS

P'edi"ive A*')& Co#p)'i(o! fo' D&!)#i

Nei%$+o'$ood (ele"io! i! PSO

0*

75

80

85

90

95

100

&,

N&,

Datasets


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&, N&,0

1020

30

40

50

60

7080

90

100

10

20

30

40

50

60

70

80

90

100

(ethodolo!

Predictive %ccurac

&, N&,0

10

20

30

40

50

60

70

80

90

100

1020

30

40

50

60

70

80

90100

(ethodolo!

Predictive %ccurac

#enses

Dataset

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RESULT ANALYSIS

Car Evaluation

Dataset


Dataset

&, N&,0

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50

60

70

80

90

100

(ethodolo!

Predictive %ccurac

&, N&,0

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50

60

70

80

90

100

(ethodolo!

Predictive %ccurac

Co!ve'%e!e R)"e Co#p)'i(o! of NPSO .i"$ PSO

0

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RESULT ANALYSIS

oo

dataset

&, N&,0

10

20

30

40

50

60

70

80

90

100

1020

30

40

50

60

70

80

90100

(ethodolo!

Predictive %ccurac

Co!ve'%e!e R)"e Co#p)'i(o! of NPSO .i"$ PSO

0

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INFERENCES

T$e )void)!e of p'e#)"*'e o!ve'%e!e )" lo)l

op"i#)l poi!"( "e!d "o e!$)!e "$e 'e(*l"(

T$e (ele"io! of lo)l +e(" p)'"ile( +)(ed o!

!ei%$+o'( ,l+e("- ')"$e' "$)! p)'"ile( o.! +e("

,p+e("- e!$)!e( "$e )*')& of "$e '*le( #i!ed

0

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Parameter Tunin!

CPS&

S%PS&

)PS&

%PS& 00

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ROLE OF CONTROL PARAMETERS

P)')#e"e' N)#e P)')#e"e' Role

I!e'"i) .ei%$" ,- Co!"'ol( "$e i#p)" of "$eveloi"& $i("o'& i!"o "$e !e.veloi"&

Aele')"io!Coeffiie!" ,-

M)i!")i!( "$e dive'(i"& of (.)'#

Aele')"io!Coeffiie!" ,=-

Co!ve'%e!e "o.)'d( "$e %lo+)lop"i#)

01

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MINING AR USING SAPSO

T$e I!e'"i) :ei%$" i! "$e veloi"& *pd)"e e7*)"io! i( #)de

)d)p"ive2SAPSO 9

SAPSO= 9

SACPSO 9

.$e'e4 g i( "$e %e!e')"io! i!de0 )!d G i( ) 'edefi!ed #)0i#*#

!*#+e' of %e!e')"io!(2 He'e4 "$e #)0i#)l )!d #i!i#)l .ei%$"( #)0)!d#i!)'e (e" "o @2 )!d @2>4 +)(ed o! e0pe'i#e!")l ("*d&2

02

A((oi)"io! R*le Mi!i!% *(i!% Self Ad)p"ive P)'"ile S.)'# Op"i#i/)"io!

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RESULT ANALYSIS

Pre$ictive Accuracy Comparison of SAPSO with PSO for +enses

Dataset

10 20 40 60 80 10050

55

60

65

70

75

80

85

90

95

100

&,

(&,1

(&

,2

)um9er of Iterations

Predictive %ccuarc 7B8

03

RESULT ANALYSIS

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RESULT ANALYSIS

10 20 40 60 80 10070

75

80

85

90

95

100

&,

(&,1

(&,2

(!&,



Pre$ictive Accuracy Comparison of SAPSO with PSO for

)a%erman/s Survival Dataset

1+

RESULT ANALYSIS

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RESULT ANALYSIS

10 20 40 60 80 10042

52

62

72

82

92

&,

(&,1

(&,2

(!&,



Pre$ictive Accuracy Comparison of SAPSO with PSO for Car

Evaluation Dataset

1*

RESULT ANALYSIS

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RESULT ANALYSIS

10 20 40 60 80 10040

50

60

70

80

90

100

&,

(&,1

(&,2

(!&,



Pre$ictive Accuracy Comparison of SAPSO with PSO for 0oo

Dataset

1-

RESULT ANALYSIS

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RESULT ANALYSIS

10 20 40 60 80 10090

91

92

93

94

95

9697

98

99

100

&,

(&,1

(&,2

(!&,



Pre$ictive Accuracy Comparison of SAPSO with PSO for

Postoperative Patient Dataset

1

INFERENCES

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INFERENCES

Self )d)p"ive #e"$od( pe'fo'# +e""e' "$)! o"$e'

#e"$od(

I! "e'# of o#p*")"io!)l effiie!& SAPSO

pe'fo'#( +e""e'

Se""i!% of )pp'op'i)"e v)l*e( fo' "$e o!"'ol

p)')#e"e'( i!volved i! "$e(e $e*'i("i( #e"$od( i("$e 1e& poi!" "o (*e(( i! "$e(e #e"$od(

1

Ph - PS& 9 d %R(

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Parameter Tunin!

CPS&

S%PS&

)PS&

%PS&1

MINING AR USING APSO

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E("i#)"io! of Evol*"io!)'& S")"e do!e *(i!% di(")!e

#e)(*'e di)!d e("i#)"o' e

Cl)((if& i!"o .$i$ (")"e p)'"ile +elo!%( )!d )d)p" "$e)ele')"io! oeffiie!"( )!d I!e'"i) :ei%$"

E0plo')"io! E0ploi")"io! Co!ve'%e!e J*#pi!% O*"

10

>(inin! %ssociation Rules usin! %daptive Particle S5arm&ptimi:ation?, Intelli!ent Computin!, )et5orJin!, andInformatics , %dvances in Intelli!ent Sstems and

Computin! $olume -, -+*, pp 31432


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Ad)p" "$e )ele')"io! oeffiie!"( )( %ive! i! ")+le

11

State %cceleration CoeLcient

c* c-

EFploration Increase 9 M Decrease 9 M

EFploitation Increase 9 N Decrease 9 N

Conver!ence Increase 9 N Increase 9 N

@umpin! out Decrease 9 M Increase 9 M


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12

T$e I!e'"i) :ei%$" i( )dQ*("ed )( %ive! i! e7*)"io!

A((oi)"io! R*le Mi!i!% "$'o*%$ Ad)p"ive P)')#e"e' Co!"'ol i! Ge!e"i

Al%o'i"$# )!d P)'"ile S.)'# Op"i#i/)"io!4 Co#p*")"io!)l S")"i("i(4Sp'i!%e'4 DOI9@2@@3(@@@8@>8@??8&2

P)')#e"e' Ad)p"io!

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0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Iteration )o.

Evaluation actor 7e8

1.5

1.6

1.7

1.8

1.9

2

2.1

2.2

2.3

2.4

!

1

6eneration )um9er

CoeLcient $alue

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Iterations

Inertia ei!ht

13

RESU#T %)%#OSIS

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P'edi"ive A*')& of Ad)p"ive PSO ove' $*!d'ed

Ge!e')"io!(

RESU#T %)%#OSIS

10 20 30 40 50 60 70 80 90 1009394

95

96

97

98

99

100

!ar

Haberan

Lens

&ostop

'oo

Iteration )um9er


2+

RESULT ANALYSIS

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P'edi"ive A*')& o#p)'i(o! of Ad)p"ive PSO .i"$

PSO

RESULT ANALYSIS

2*

0

10

20

30

40

50

60

70

80

90

100

&,

(&,

Datasets


RESULT ANALYSIS

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Co#p)'i(o! of N*#+e' of R*le( Mi!ed +& APSO )!d PSO

RESULT ANALYSIS

2-

0

5

10

15

20

25

30

35

40

&,

(&,

Datasets

)um9er of Rules (ined

RESULT ANALYSIS

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Co#p)'i(o! of E0e*"io! Ti#e of APSO )!d PSO

RESULT ANALYSIS

2

!ar "#a$uat%on Haberan Lens &ostop 'oo10

5010

10010

15010

20010

25010

30010

35010

40010

45010

50010

&,

(&,

Datasets

EFecution Time 7ms8

RESU#T

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Co#p)'i(o! of Co!ve'%e!e of APSO )!d PSO

RESU#T%)%#OSIS

2

0

20

40

60

80

100

120

140

160

(&,

&,

Datasets

Iteration )um9er

INFERENCES

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INFERENCES

D)") Depe!de!" )d)p"io! e!$)!e( "$e)*')& of "$e )((oi)"io! '*le( #i!ed

Ad)p"io! +)(ed o! (")"e of "$e p)'"ile+)l)!e( "$e e0plo')"io! )!d e0ploi")"io!

I!e'"i) .ei%$" )d)p"io! +)(ed o! fi"!e(( v)l*e(#)i!")i!( "$e %lo+)l (e)'$ (p)e

2

Phase 4 Com9ination

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Phase 4 Com9ination(ethods

PS& S=#%

6PS& 76%/PS&

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)1B"2D GA3 PSO 'GPSO( #ODE+

6enetic %l!orithm Particle S5arm&ptimi:ation

Advantages

6lo9al &ptimi:ation EasConver!ence

6% 5orJs on a

population ofpossi9le solution

PS& have no

overlappin! andmutationcalculation

Disadvanta

ges

Cannot assureconstant

optimisationresponse times

The methodeasil suQers

from the partialoptimism

(utation andCrossover at timescreates children

fara5a from !ood

eaJ localsearch a9ilit

21


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Uppe'

Lo.e'

Ge!e"iAl%o'i"$#

P)'"ile S.)'#Op"i#i/)"io!

Ev)l*)"eFi"!e((

I!i"i)l

Pop*l)"io!

R)!1ed

Pop*l)"io!

Upd)"ed

Pop*l)"io!

22

Mi!i!% A((oi)"io! R*le( *(i!% H&+'id Ge!e"i Al%o'i"$# )!d P)'"ile S.)'#Op"i#i/)"io! Al%o'i"$# ,GPSO-4 I!"e'!)"io!)l Jo*'!)l of D)") A!)l&(i("e$!i7*e( )!d ("')"e%ie(4 I!de'(ie!e 4 ,Aep"ed-

RESULT ANALYSIS

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RESULT ANALYSIS

P'edi"ive A*')& Co#p)'i(o! Of GPSO .i"$ GA)!d PSO

23

0

10

20

30

40

50

60

70

80

90

100

(

&,

&,

Datasets


RESULT ANALYSIS

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( &, &,0

10

20

30

40

50

6070

80

90

100

10

20

30

40

50


( &, &,0

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50


C)' Ev)l*)"io!D)")(e"

H)+e'#)!

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( &, &,0

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50


( &, &,0

10

20

30

40

50

60

70

80

90

100

10 20

30 40

50


Po(" ope')"ivep)"ie!" D)")(e" Le!(e( D)")(e"

RESULT ANALYSIS

P'edi"ive A*')& Co#p)'i(o! Of GPSO .i"$GA )!d PSO

3*

RESULT ANALYSIS

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( &, &,0

10

20

30

40

50

60

70

80

90

100

10

20

30

40

50


Zoo D)")(e"

RESULT ANALYSIS

P'edi"ive A*')& Co#p)'i(o! Of GPSO .i"$GA )!d PSO

3-

INFERENCES

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INFERENCES

Ge!e')"e( '*le .i"$ +e""e' p'edi"ive )*')&

.$e! o#p)'ed "o GA )!d PSO

Glo+)l (e)'$ op"i#i/)"io! of GA )!d po.e'f*l

("o$)("i op"i#i/)"io! offe'ed +& PSO )'e +o"$

o#+i!ed i! GPSO4 'e(*l"i!% i! )((oi)"io! '*le(.i"$ o!(i("e!" )*')&

3

Phase 4 Com9ination

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Phase Com9ination(ethods

PS& S=#%

6PS& 76%/PS&

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Mi!i!% AR *(i!% PSO SFLA

S$*ffled F'o% Le)pi!% Al%o'i"$# ,SFLA- i( )dop"ed "ope'fo'# "$e lo)l (e)'$

He'e "$e p)'"ile( )'e )llo.ed "o %)i! (o#e

e0pe'ie!e4 "$'o*%$ ) lo)l (e)'$4 +efo'e +ei!%i!volved i! "$e evol*"io!)'& p'oe((

T$e ($*ffli!% p'oe(( )llo.( "$e p)'"ile( "o %)i!

i!fo'#)"io! )+o*" "$e %lo+)l +e("2

3

Me)(*'e( fo' I#p'ovi!% P'e#)"*'e Co!ve'%e!e i! P)'"ile S.)'#Op"i#i/)"io! fo' A((oi)"io! R*le Mi!i!%4%ccepted in Jo*'!)l ofCo#p*"i!% Te$!olo%&2

=#&C

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& C & SOSFLA

Generation of initial population(P) and evaluating

the fitness of each particle

Velocity and position updation of particles

Distribution of frog into M memeplexes

Iterative pdating of !orst frog in each

memeplexes

"ombining all frogs to form a ne! population

#ermination

criteria satisfied$

Determine the best solution

%orting the population in descending order in

terms of fitness value

SFLA

30

Fo'#)"io! of Me#eple0e(

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p

ro 6

Memeplex1

Memeplex2

Memeplex3

ro 1

ro 2

ro 3

ro 7

ro 5

ro 4

ro 8

Sorted=ro!s

31

Upd)"io! of :o'(" P)'"ile(

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5+ 8 Po(i"io! of "$e %'o*p +e(" 3%lo+)l +e("

5. 8 Po(i"io! of "$e .o'(" f'o% i! "$e %'o*pDi 8 C)l*l)"ed !e. po(i"io! of "$e .o'(" f'o%

T$e po(i"io! of "$e p)'"ile( .i"$ .o'(" fi"!e(( i(#odified *(i!%

p

98

RESULT ANALYSIS

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RESULT ANALYSIS

P'edi"ive A*')& Co#p)'i(o!

33

0

10

20

30

40

50

60

70

80

90

100

&,

&, L(

(&,

(&, L(

Datasets


RESULT ANALYSIS

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RESULT ANALYSIS

0

1

2

3

4

5

#enses Dataset

&,

(&,

&,L(

(&,L(

Iteration )um9er

=itness $alue

Fi"!e(( V)l*e Co#p)'i(o!

*++

RESULT ANALYSIS

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RESULT ANALYSIS

00.5

11.5

22.5

33.5

4

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RESULT ANALYSIS

0

1

2

3

4

5

Car Evaluation Dataset

&,

(&,

&,L(

(&,L(

Iteration )um9er

=itness $alue


*+-

RESULT ANALYSIS

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RESULT ANALYSIS

0

1

2

3

4

5


&,

(&,

&,L(

(&,L(

Iteration )um9er

=itness $alue


*+

RESULT ANALYSIS

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RESULT ANALYSIS

0

0.5

1

1.5

2

2.53

3.5

4

oo Dataset

&,

(&,

&,L(

(&,L(

Iteration )um9er

=itness $alue


*+

INFERENCES

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APSO SFLA (ee#( "o %ive '*le( .i"$ +e""e')*')"e 'e(*l"( "$)! !o'#)l PSO

T$e Lo)l (e)'$ #)i!")i!( "$e %lo+)l (e)'$(p)e effe"ivel& "$e'e+& i!'e)(i!% "$epe'fo'#)!e of ARM

*+

Phase 4 Com9ination

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(ethods

PS& S=#%

6PS& 76%/PS&

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*+1

I! PSO4 "$e (")"e of ) p)'"ile i( depi"ed +& i"(

position vector (xi) and velocity vector (vi). I! *)!"*# L).( of Me$)!i(4 )o'di!% "o

Uncertainty Principle4 0i )!d vi)!!o" +e de"e'#i!ed(i#*l")!eo*(l&2

I! "$e 7*)!"*# #odel of PSO4 "$e (")"e of ) p)'"ilei( depi"ed +& .)ve8 f*!"io! ,04"-2

Revie. of P)'"ile S.)'# Op"i#i/)"io! 9 B)(i Co!ep"(4 V)'i)!"( )!dAppli)"io!( 4 Aep"ed i! I!"e'!)"io!)l Jo*'!)l of Adv)!ed I!fo'#)"io! )!dCo##*!i)"io! Te$!olo%&

A Co#p)')"ive S"*d& o! "$e A((oi)"io! R*le Mi!i!% Al%o'i"$#(

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*+2

PSO M "$ d l

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109

PSO Me"$odolo%&

T$e p)'"ile( #ove#e!" i( +&9

:$e'e4p , pid ,8- p%d- , '-3 ,' ='=-

W i( "$e o!"')"io!8e0p)!(io! oeffiie!" X@4

PSO FLO:CHART

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YES

**+

Start

Initiali:e thes5arm

Calculate mean 9est7m9est8

Update particle position

Update local 9estUpdate !lo9al 9est

Termination criteriareached

Stop

No

uantu e/a#%our

EPSO Me"$odolo%&

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111

&P%'

QPSO

%ystematic

Parameter

daptation

Local

%earch

#echniues

DP#IV*

&P%'

M*M*#I"

&P%'

*V'L#I'+,-

&+#M

.*/V*D P%'

EPSO Me"$odolo%&

A! Evol*"io!)'& *)!"*# Be$)ved P)'"ile S.)'# Op"i#i/)"io! fo' Mi!i!%A((oi)"io! R*le(

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112

Generation of initial population(P) and evaluating thefitness of each particle

MSFLA

QPSO

Adapting

Parameters

Distribution of frog into M memeplexes

Iterative pdating of !orst frog in each memeplexes

"ombining all frogs to form a ne! population

#ermination

criteria

satisfied$

Determine the best solution

Sorting the population in descending order in terms of

fitness value

Position updation of particles

%daptation of acceleration coefficient and contraction

expansion coefficient

EPSO Me"$odolo%&

RESULT ANALYSIS

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**

P'edi"ive A*')& Co#p)'i(o!

INFERENCES

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EPSO pe'fo'#( +e""e' i! "e'#( of p'edi"ive)*')& )*')"e o#p)'ed "o PSO2

T$e Lo)l (e)'$ #)i!")i!( "$e %lo+)l (e)'$(p)e effe"ivel& )!d )void)!e of ".o(i#*l")!eo*( e7*)"io!( e!$)!e( "$e

pe'fo'#)!e of "$e #e"$odolo%&2

**

PERFORMANCE ANALYSIS

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Co#p)'i(o! of "$e P'edi"ive A*')&

A$ieved +& All Me"$od(

**

75

80

85

90

95

100

Lenses

Haberan

!ar"#a$uat%on

&ost,p

'oo

(ethodolo!



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Co#p)'i(o! of "$e N*#+e' of R*le( Mi!ed +&

All Me"$od(

**0

*+

0+

**+

*0+

-*+

-0+

*+

0+

#enses

Post&p

oo

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Co#p)'i(o! of "$e E0e*"io! Ti#e fo'

All Me"$od(

**1

0

20

40

60

80

100

120

140

160

Lenses

&ost,p

'oo

Haberan

!ar"#a$uat%on

(ethodolo!

Time in Sec


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Dataset %6%

6%5ithElitism

PS& CPS& )PS& S%PS& %PS& 6PS&%PS&S=#%

EPS&

Lenses 0.52 0.50 0.50 0.52 0.66 0.538 0.5 0.66 0.538 0.529

Haberansur#%#a$

0.52 0.66 0.53 0.5 0.511 0.505 0.66 0.52 0.501 0.505

!ar"#a$uat%on 0.62 0.51 0.5 0.5 0.644 0.544 0.52 0.64 0.504 0.505

&ostoperat%#e

0.5 0.56 0.50 0.5 0.625 0.519 0.544 0.644 0.502 0.511

'oo 0.64 0.51 0.5 0.5 0.56 0.521 0.502 0.547 0.502 0.512

Co#p)'i(o! of L)pl)e Me)(*'e fo' All Me"$od(

**2


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Dataset %6%

6%5ithElitism

PS& CPS& )PS&S%PS&%PS& 6PS&%PS&S=#%

;PS&E;PS&

Lenses 1 1.89 3.26 2 1 1.84 2.08 1.043 1.14 1.5 1.6

Haberansur#%#a$

2 1 1.84 2.08 1.17 1.164 1.043 1 1.894 3.268 1.037

!ar"#a$uat%on

1 1.208 2.08 1.05 1 1.07 1 0.998 1.121 3.181 1

&ostoperat%#e

6.31 1.98 5.05 1.279 1 1.20 1.07 1.06 1.31 3.954 1.011

'oo 1.06 1.208 1.279 6.314 1.98 1.16 1.31 1.08 5.05 4.04 1.01

Co#p)'i(o! of Lif" Me)(*'e fo' All Me"$od(

**3


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Dataset %6%

6%5ithElitism


;PS&E;PS&

Lenses 1

Haberansur#%#a$

1

!ar"#a$uat%on

NaN

2

0.8 NaN

&ostoperat%#e

NaN

'oo

Co#p)'i(o! of Co!vi"io! Me)(*'e fo' All Me"$od(

*-+


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Dataset %6%

6%5ithElitism


;PS&E;PS&

Lenses 0.00

2 0.003 0.015 0.062 0 0.076 0.006 0 0.020 0.041 0.125

Haberansur#%#a$

0.062

0 0.076 0.006 0.007 0.003 0 0.002 0.003 0.015 0.033

!ar"#a$uat%on 0 0.013 0.006 0.001 0.003 0.012 0.002 0 0.002 0.016 0

&ostoperat%#e

0.016

0.137 0.007 0.007 0 0.013 0.013 0.046 0.002 0.034 0.0009

'oo 0.04

6 0.013 0.007 0.016 0.137 0.012 0.002 0.084 0.007 0.037 0.002

Co#p)'i(o! of Leve')%e Me)(*'e fo' All Me"$od(

*-*


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GA with Elitism for A"##ining Association rules %ase$ on chaotic #aps

&eigh%orhoo$ selection in PSO for A"#


)y%ri$ization of GA an$ PSO

#emetic PSO with Shuffle$ *rog +eaping Algorithm,uantum Behave$ PSO for A"#

*--

CONCLUSION

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H&+'id #odel p'od*e( o!(i("e!" 'e(*l"(

Ge!e"i Al%o'i"$# pe'fo'#( +e""e' "$)! "')di"io!)l e0i("i!%

#e"$od(P)'"ile (.)'# op"i#i/)"io! p'od*e( 'e(*l"( lo(e' "o GA

Ad)p"ive #e$)!i(# pe'fo'#( +e""e' "$)! #odifi)"io! i!

#e"$od(

*-

:$e! lo)l (e)'$ i!"'od*ed i! PSO 'e(*l"( i! +e""e'

pe'fo'#)!e i! "e'# of )*')& )!d !*#+e' of '*le(

*)!"*# +e$)vio' .$e! i!"'od*ed i! PSO pe'fo'#( +e""e' "$)! )ll o"$e' #e"$od(

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;2I!di') )!d S2;)!#)!i4 F')#e.o'1 fo' Co#p)'i(o! of A((oi)"io! R*leMi!i!% U(i!% Ge!e"i Al%o'i"$#4 P)'" 4 8- =@2

;2I!di')4 S2;)!#)!i4 P')()!"$4 H)'i($ )!d Jeev)4 Pop*l)"io! B)(edSe)'$ Me"$od( i! Mi!i!% A((oi)"io! R*le(< 4 I! 9 T$i'dI!"e'!)"io!)lCo!fe'e!e o! Adv)!e( i! Co##*!i)"io!4 Ne".o'14 )!d Co#p*"i!%4LNICST pp2 ==4 =@=2

*-

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;2I!di')4 S2;)!#)!i4 A(.i!i4 R)!%)l)1($#i4 S*#i"$')4 Div&)#)'&4 Mi!i!%

A((oi)"io! R*le( *(i!% Ad)p"ive P)'"ile S.)'# Op"i#i/)"io!4 I!"elli%e!"Co#p*"i!%4 Ne".o'1i!%4 )!d I!fo'#)"i(4 Adv)!e( i! I!"elli%e!" S&("e#()!d Co#p*"i!% Vol*#e =>?4 pp 8>4 =@>

;2I!di')4 S2;)!#)!i4 R2 J)%)!4 G2 B)l)Qi4 F2 Mil"o! Jo(ep$4 A Co#p)')"iveS"*d& o! "$e A((oi)"io! R*le Mi!i!% Al%o'i"$#(4 i! TEIP (po!(o'ed

NCIT

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;2I!di') )!d S2;)!#)!i4 Pe'fo'#)!e A!)l&(i( of Ge!e"i Al%o'i"$#fo' Mi!i!% A((oi)"io! R*le(4 IJCSI I!"e'!)"io!)l Jo*'!)l of Co#p*"e'

Sie!e I((*e(4 Vol2 4 I((*e =4 No 4 ?8?4 M)'$ =@=

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;2I!di') )!d S2;)!#)!i4 A((oi)"io! R*le Mi!i!% +& D&!)#iNei%$+o'$ood Sele"io! i! P)'"ile S.)'# Op"i#i/)"io!4 I!"e'!)"io!)lJo*'!)l of Adv)!ed I!fo'#)"io! Sie!e )!d Te$!olo%& 4 Vol24 No24Nove#+e' =@=

;2I!di') )!d S2;)!#)!i4 A((oi)"io! R*le Mi!i!% *(i!% Self Ad)p"iveP)'"ile S.)'# Op"i#i/)"io!

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;2I!di') )!d S2;)!#)!i4 Me)(*'e( fo' I#p'ovi!% P'e#)"*'e Co!ve'%e!e

i! P)'"ile S.)'# Op"i#i/)"io! fo' A((oi)"io! R*le Mi!i!%4.Aep"ed i!Jo*'!)l of Co#p*"i!% Te$!olo%&2

;2I!di') )!d S2;)!#)!i4 Mi!i!% A((oi)"io! R*le( *(i!% H&+'id Ge!e"iAl%o'i"$# )!d P)'"ile S.)'# Op"i#i/)"io! Al%o'i"$# ,GPSO-4I!"e'!)"io!)l Jo*'!)l of D)") A!)l&(i( "e$!i7*e( )!d ("')"e%ie(4

I!de'(ie!e ,Aep"ed-2 ;2I!di') )!d S2;)!#)!i4 A((oi)"io! R*le Mi!i!% "$'o*%$ Ad)p"ive

P)')#e"e' Co!"'ol i! Ge!e"i Al%o'i"$# )!d P)'"ile S.)'# Op"i#i/)"io!4Co#p*")"io!)l S")"i("i( 4 DOI9@2@@3(@@@8@>8@??8&2

;2I!di')4 S2;)!#)!i4 Revie. of P)'"ile S.)'# Op"i#i/)"io! 9 B)(iCo!ep"(4 V)'i)!"( )!d Appli)"io!( 4 Aep"ed i! I!"e'!)"io!)l Jo*'!)lof Adv)!ed I!fo'#)"io! )!d Co##*!i)"io! Te$!olo%&2

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;2 I!di')4 S2 ;)!#)!i4 R2 J)%)!4 G2 B)l)Qi4 F2 Mil"o! Jo(ep$4 A!

Evol*"io!)'& *)!"*# Be$)ved P)'"ile S.)'# Op"i#i/)"io! fo' Mi!i!%A((oi)"io! R*le(4 I!"e'!)"io!)l Jo*'!)l of Sie!"ifi E!%i!ee'i!%Re(e)'$4 Vol24 I((*e 4 pp2 ?8?4 =@>2

*-2

References Ji!% Li4 H)! R*i8fe!%4 A Self8Ad)p"ive Ge!e"i Al%o'i"$# B)(ed O!

R l C d d I " "i l C f Bi di l E i i d

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Re)l8 Coded4 I!"e'!)"io!)l Co!fe'e!e o! Bio#edi)l E!%i!ee'i!% )!d

o#p*"e' Sie!e 4 P)%e,(-9 8 > 4 =@@

C$*)!8;)!% Ti!%4 :ei8Mi!% Ze!%4 T/*8 C$ie$ Li!4 Li!1)%e Di(ove'&"$'o*%$ D)") Mi!i!%4 IEEE M)%)/i!e o! Co#p*")"io!)l I!"elli%e!e4Vol*#e 4 Fe+'*)'& =@@2

C)i(e(4 Y24 Le&v)4 E24 Go!/)le/4 A24 Pe'e/4 R24 A! e0"e!(io! of "$eGe!e"i I"e')"ive App'o)$ fo' Le)'!i!% R*le S*+(e"( 4 >"$I!"e'!)"io!)l :o'1($op o! Ge!e"i )!d Evol*"io!)'& F*//& S&("e#(4P)%e,(-9 ? 8 4 =@@

S$)!%pi!% D)i4 Li G)o4 i)!% Z$*4 C$)!%.* Z$*4 A Novel Ge!e"i

Al%o'i"$# B)(ed o! I#)%e D)")+)(e( fo' Mi!i!% A((oi)"io! R*le(4 "$IEEE3ACIS I!"e'!)"io!)l Co!fe'e!e o! Co#p*"e' )!d I!fo'#)"io!Sie!e4 P)%e,(-9 @4 =@@

Pe'e%'i!4 A24 Rod'i%*e/4 M2A24 Effiie!" Di("'i+*"ed Ge!e"i Al%o'i"$#fo' R*le E0"')"io!42 Ei%$"$ I!"e'!)"io!)l Co!fe'e!e o! H&+'id

I!"elli%e!" S&("e#(4 HIS @2 P)%e,(-9 ? ?4 =@@*-3

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ThanJOou

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;ueries G

Def! E7*)"io! R)!%e

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Rule (easures

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