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Empirical Study on Mining Association Rules

Using Population Based Stochastic Search

Algorithms

RESEARCH PROPOSAL

K.INDIRA

Under the guidance of

Dr. S.KANMANIProfessor,

Department of Information Technology,

Pondicherry Engineering College


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ORGANIZATION

OBJECTIVES

WHY ASSOCIATION RULES

INTRODUCTION

MOTIVATION

EMPRICAL STUDY

CONCLUSION

PUBLICATIONS

REFERNECES


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3INDIAN INSTITUTE OFTECHNOLOGY, ROORKEE

1.OBJECTIVES

Exploration of usage of evolutionary algorithms for

mining association rules.

To develop efficient methodology for mining Association

rules both effectively and efficiently using population

based search methods namely Genetic Algorithm (GA) and

Particle Swarm Optimization(PSO).

To hybridize GA and PSO for mining association rules to

overcome each others weakness, leading to new

approach for association rule mining.


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2.WHY ASSOCIATION RULE MINING

Large quantities of data is being accumulated.

There is a huge gap from the stored data to the

knowledge that could be construed from the data

Searching for meaningful information in largedatabases has become a very important issue.

Association rules, Clustering and Classification are

methods applied for extracting information fromdatabases.

Association rule mining is the most widely applied

method.


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3. INTRODUCTION

3.1 DATA MINING

Extraction of interesting information orpatterns from data in large databases is known

as data mining.


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3.2 ASSOCIATION RULE MINING

Association rule mining finds interesting

associations and/or correlation relationshipsamong large set of data items.


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3.3 ASSOCIATION RULES

Association Rules are of form X Y with twocontrol parameters support and confidence

Support,s, probability that a transactioncontains X Y

Confidence, c,conditional probability that atransaction having X also contains Y


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3.3 ASSOCIATION RULES

Tid Items bought

10 Milk, Nuts, Sugar

20 Milk, Coffee, Sugar

30 Milk, Sugar, Eggs

40 Nuts, Eggs, Bread50 Nuts, Coffee, Sugar , Eggs,

Bread

Customer

buys sugar

Customerbuys both

Customer

buys milk

Let minsup = 50%, minconf = 50%

Freq. Pat.: Milk:3, Nuts:3, Sugar:4, Eggs:3, {Milk, Sugar}:3

Association rules: Milk Sugar (60%, 100%) Sugar Milk (60%, 75%)


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4. MOTIVATION

4.1 EXISTING SYSTEMApriori, FP Growth Tree, clat are some of the

popular algorithms for mining ARs.

Traverse the database many times.

I/O overhead, and computational complexity is

more.

Cannot meet the requirements of large-scale

database mining.

Does not fit in memory and is expensive to build


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4.2 EVOLUTIONARY ALGORITHM

Applicable in problems where no (good) method is

available:

Discontinuities, non-linear constraints, multi-

modalities.

Discrete variable space.

Implicitly defined models (if-then-else constructs).

Noisy problems.

Most suitable in problems where multiple solutions are

required:

Multi-modal optimization problems.

Multi-objective optimization problems.


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4.2 EVOLUTIONARY ALGORITHM

Parallel implementation is easier.

Evolutionary algorithms provide robust and efficient

approach in exploring large search space.


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4.3 GA AND PSO : AN INTRODUCTION

Genetic algorithm (GA) and Particle swarm

optimization (PSO) are both population based

search methods and move from set of points

(population) to another set of points in a single

iteration with likely improvement using set of

control operators.


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4.4 GENETIC ALGORITHM

A Genetic Algorithm (GA) is a procedure used to

find approximate solutions to search problems

through the application of the principles of

evolutionary biology.


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4.2 PARTCILE SWARM OPTIMIZATION

PSOs mechanism is inspired by the social and

cooperative behavior displayed by various

species like birds, fish etc including human

beings.


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Association Rule

(AR) Mining

Population BasedEvolutionary Methods

Genetic Algorithm

(GA)Particle Swarm

Optimization (PSO)

Mining Association

Rules using GA

Analyzing the roleofControl parameters in

GA for mining ARs

Mining ARs using

Self Adaptive GA

Elitist GA for

AssociationRule

Mining

Mining Association

rules with PSO

Mining Association

Rules with chaotic

PSO

Mining Association

rules with DynamicNeighborhood

Selection in PSO

Mining Associationrules with Self

Adaptive PSO

Hybrid GA/PSO

(GPSO) for AR

Mining

5. BLOCK DIAGRAM OF RESEARCH

MODULES


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5.1 DATASETS DESCRIPTION

Lenses

Habermans Survival

Car Evaluation

Post operative care

Zoo


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5.1 LENSES DATASET

Age of thepatient

1: Young 2: Presbyopic 3:Presbyopic

SpectaclePrescription

1: Myopic 2:Hypermetropic

Astigmatic 1: No 2: Yes

TearProduction

Rate

1: Reduced 2: Normal

Result 1: HardContactlenses

2: Soft ContactLenses

3: No lenses


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5.1 HABERMANS SURVIVAL DATASET

Age of the patient 30-83Numeric

Patient's year ofoperation

NumericEg. 67

Number of positiveaxillary nodes

detected

0-46Numeric

Result 1= the patientsurvived 5 years or

longer s

2 = the patient diedwithin 5 year


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5.1 CAR EVALUATION DATASET

Buying price Very high High Medium Low

MaintenancePrice

Very high High Medium Low

Doors 2 3 4 5

Persons 2 4 More

Luggage boot Small Big Medium

Safety Low Medium High

Result Unacceptable Acceptable Good Verygood


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5.3.1 MINING AR USING GA

MethodologySelection : Tournament

Crossover Probability : Fixed ( Tested with 3 values)

Mutation Probability : No Mutation

Fitness Function :

Dataset : Lenses, Iris, Haberman from

UCI Irvinerepository.

Population : Fixed ( Tested with 3 values)


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Flow chart of the GA


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R lt A l i


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Results Analysis

No. of Instances No. of Instances * 1.25 No. of Instances *1.5

Accuracy

%

No. of

Generations

Accuracy

%

No. of

Generations

Accuracy

%

No. of

Generations

Lenses 75 7 82 12 95 17

Haberman 71 114 68 88 64 70

Iris 77 88 87 53 82 45

Comparison based on variation in population Size.

Minimum Support & Minimum Confidence

Sup = 0.4 & con =0.4 Sup =0.9 & con =0.9 Sup = 0.9 & con = 0.2 Sup = 0.2 & con = 0.9

Accuracy

%

No. of

Gen

Accuracy

%

No. of

Gen.

Accuracy

%

No. of

Gen.

Accuracy

%

No. of

Gen

Lenses 22 20 49 11 70 21 95 18

Haberman 45 68 58 83 71 90 62 75

Iris 40 28 59 37 78 48 87 55

Comparison based on variation in Minimum Support and Confidence


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Cross Over

Pc = .25 Pc = .5 Pc = .75

Accurac

y %

No. of

Generations

Accuracy % No. of

Generations

Accuracy

%

No. of

Generations

Lenses 95 8 95 16 95 13

Haberman 69 77 71 83 70 80

Iris 84 45 86 51 87 55

Dataset No. of

Instances

No. of

attributes

Population

Size

Minimum

Support

Minimum

confidence

Crossover

rate

Accuracy

in %

Lenses 24 4 36 0.2 0.9 0.25 95

Haberman 306 3 306 0.9 0.2 0.5 71

Iris 150 5 225 0.2 0.9 0.75 87

Comparison of the optimum value of Parameters for

maximum Accuracy achieved

Comparison based on variation in Crossover Probability


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Population Size Vs Accuracy

Minimum Support and Confidence Vs Accuracy


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Values of minimum support, minimum confidence and mutation

rate decides upon the accuracy of the system than other GA

parameters

Crossover rate affects the convergence rate rather than the

accuracy of the system

The optimum value of the GA parameters varies from data to

data and the fitness function plays a major role in optimizing the

results

Inferences


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Mining ARs using Self Adaptive GA in Java.

Methodology

Selection : Roulette Wheel

Crossover Probability : Fixed ( Tested with 3 values)

Mutation Probability : Self Adaptive

Fitness Function :

Dataset : Lenses, Iris, Car from

UCI Irvine repository.

Population : Fixed ( Tested with 3 values)

Procedure SAGA


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Procedure SAGA

Begin

Initialize population p(k);

Define the crossover and mutation rate;

Do

{

Do

{

Calculate support of all k rules;

Calculate confidence of all k rules;Obtain fitness;

Select individuals for crossover / mutation;

Calculate the average fitness of the n and (n-1) the generation;

Calculate the maximum fitness of the n and (n-1) the generation;

Based on the fitness of the selected item, calculate the new crossoverand mutation rate;

Choose the operation to be performed;

} k times;

}


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Self Adaptive GA

SELF ADAPTIVE

Results Analysis


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Dataset Traditional GA Self Adaptive GA

Accuracy No. of Generations Accuracy No. of Generations

Lenses 75 38 87.5 35

Haberman 52 36 68 28

Car Evaluation 85 29 96 21

Dataset Traditional GA Self Adaptive GA

Accuracy No. ofGenerations

Accuracy No. of Generations

Lenses 50 35 87.5 35

Haberman 36 38 68 28

Car

Evaluation

74 36 96 21

ACCURACY COMPARISON BETWEEN GA AND SAGA WHEN PARAMETERS ARE

SET TO TERMINATION OF SAGA

ACCURACY COMPARISON BETWEEN GA AND SAGA WHEN PARAMETERS ARE IDEAL

FOR TRADITIONAL GA

Results Analysis

ACCURACY COMPARISON BETWEEN GA AND SAGA WHEN


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0

10

20

30

40

50

60

70

80

90

100

Lenses Haberman Car Evaluation

PredictiveAccuracy(%)

Dataset

Traditional GA Accuracy

Self Adaptive GA Accuracy

ACCURACY COMPARISON BETWEEN GA AND SAGA WHEN

PARAMETERS ARE IDEAL FOR TRADITIONAL GA


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0

10

20

30

40

50

60

70

80

90

100

Lenses Haberman Car Evaluation


Dataset

Traditional GA

Self Adaptive GA

ACCURACY COMPARISON BETWEEN GA AND SAGA

WHEN PARAMETERS ARE ACCORDING TO

TERMINTAION OF SAGA

I f


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Inferences

Self Adaptive GA gives better accuracy than Traditional GA.

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GA with Elitism for Mining ARsMethodology

Selection : Elitism with roulette wheel

Crossover Probability : Fixed to Pc

Mutation Probability : Self Adaptive

Fitness Function : Fitness(x) = con(x)*(log(sup(x) *

length(x) + 1)

Dataset : Lenses, Iris, Car from UCI Irvine

repository.

Population : Fixed38


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No. Of

Iterations

Lenses Car Evaluation Haberman

4 90 94.4 706 87.5 91.6 75

8 91.6 92.8 91.6

10 90 87.5 75

15 87.5 90 83.3

20 91.6 87.5 91.6

25 87.5 87.5 92.5

30 83.3 93.75 83.3

50 90 75 75

Predictive Accuracy for Mining AR based on GA with Elitism

Results Analysis

39

P di i A f Mi i AR b d GA


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0

10

20

30

40

50

60

70

80

90

100

6 8 10 15 20 25 30 50


No. of Iterations

Lenses

Car Evaluation

Haberman

Predictive Accuracy for Mining AR based on GA

with Elitism


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No of matches vs. No of iterations


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No. Of

Iterations

Lenses (ms) Car Evaluation

(ms)

Haberman

(ms)

4 15 547 125

6 16 721 156

8 31 927 187

10 31 1104 20315 32 1525 281

20 47 1967 359

25 63 2504 421

30 78 2935 53050 94 4753 998

Execution Time for Mining AR based on GA with Elitism

42


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0

1000

2000

3000

4000

5000

6000

4 6 8 10 15 20 25 30 50

Executiontime(ms)

No. of Iterations

Haberman (ms)

Car Evaluation (ms)

Lenses (ms)


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Inferences

Marginally better accuracy arrived

Computational Efficiency found to be optimum

Elitism when introduced helps in retaining

chromosomes with good fitness values for next

generation

44


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Mining ARs using PSO

Methodology

Each data itemset are represented as particles

The particles moves based on velocity

The particles position are updated based on

S O ( SO)


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Particle Swarm Optimization (PSO)

Flow chart depicting the General PSO Algorithm:

Start

Initialize particles with random position

and velocity vectors.

For each particles position (p)evaluate fitness

If fitness(p) better than

fitness(pbest) then pbest= pLoopuntilall

particlesexhaus

t

Set best of pBests as gBest

Update particles velocity (eq. 1) and

position (eq. 3)

Loopun

tilmaxiter

Stop:giving gBest, optimal solution.

Results Analysis


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Dataset NameTraditional

GA

Self

AdaptiveGA

PSO

Lenses 87.5 91.6 92.8

Haberman 75.5 92.5 91.6

Car evaluation 85 94.4 95

Results Analysis

0

200

400

600

800

1000

1200

4 6 8 10 15 20 25 30 50

Execution

Timemsec

No. of iterations

Haberman

PSO

SAGA0

20

40

60

80

100

4 6 8 101520253050

Executio

nTimemsec

No. of iterations

Lenses

PSO

SAGA

0

200

400

600

800

1000

1200

4 6 8 10 15 20 25 30 50

ExecutionTimemsec

No. of Iterations

Car Evaluation

PSO

SAGA

Predictive Accuracy

Execution Time

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Inferences


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Inferences

PSO produce results as effective as self adaptive GA

Computational effectiveness of PSO marginally fast when

compared to SAGA.

In PSO only the best particle passes information to others and

hence the computational capability of PSO is marginally better

than SAGA.

48BACK

Mining ARs using Chaotic PSO


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Mining ARs using Chaotic PSO

The new chaotic map model is formulated as

Methodology

Initial point u0and V0to 0.1

The velocity of each particle is updated by

Mining ARs using


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Compute xi(k+1)Compute (f(xi(k+1))

Reorder the particles

Generate neighborhoods I =1

k K

i = i +1

K = k+1

Start

K =1

Initialize xi(k), vi(k)

Compute f(xi(k))

Determine best particles in the

neighborhood of i

Update previous best if necessary

I N

Stop

no

no

yes

yes

Mining ARs using

Chaotic PSO

50

ACCURACY COMPARISON


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80

82

84

86

88

90

92

94

96

98

100

Haberman lens car evaluation

PredictiveAccura

cy(%)

SAGA

PSO

CPSO

ACCURACY COMPARISON

C R t C i f L


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75

80

85

90

95

100

SAGA pso cpso

4

6

8

10

15

20

25

30

50

Convergence Rate Comparison for Lenses

Convergence Rate Comparison for Car


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40

50

60

70

80

90

100

SAGA pso cpso

4

6

8

10

15

20

25

30

50

g p

Evaluation

C R t C i f H b


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0

10

20

30

40

50

60

70

80

90

100

SAGA pso cpso

4

6

8

10

15

20

25

30

50

Convergence Rate Comparison for Habermans

Survival

Inferences


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Inferences

Better accuracy than PSO

The Chaotic Operators could be changed by altering the initial

values in chaotic operator function

The balance between exploration and exploitation is

maintained

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Mining ARs using Neighborhood Selection


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g g g

in PSOMethodology

The concept of local best particle (lbest) replacing the particle

best (pbest) is introduced

The neighborhood best (lbest) selection is as follows;

Calculate the distance of the current particle from other

particles

Find the nearest m particles as the neighbor of the current

particle based on distance calculated

Choose the local optimum lbest among the neighborhood

in terms of fitness values


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Interestingness Measure

The interestingness measure for a rule is taken from relativeconfidence and is as follows:

Where k is the rule, x the antecedent part of the rule and y

the consequent part of the rule k.

Predictive Accuracy Comparison for Dynamic


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88

89

90

91

92

93

94

95

96

97

98

Haberman lens car evaluation

PredictiveAcurac

y(%)

saga

pso

Npso

Neighborhood selection in PSO


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Dataset Interestingness Value

Lens 0.82

Car Evaluation 0.73

HabermansSurvival 0.8

Measure of Interestingness

Execution Time Comparison for Dynamic


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0

200

400

600

800

1000

1200

1400

1600

4 6 8 10 15 20 25 30 50

Lenses PSO

Lenses NPSO

Haberman's Survival PSO

Haberman's Survival NPSO

Car Evaluation PSO

Car Evaluation NPSO

Neighborhood selection in PSO

Predictive Accuracy over Generation for a) Car


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0

10

20

30

40

50

60

70

80

90

100

PSO NPSO


4

6

8

10

15

20

25

30

50

Evaluation b) Lenses c) Habermans Survival datasets

0

10

20

30

40

50

60

70

80

90

100

PSO NPSO


4

6

8

10

15

20

25

30

50

0

10

20

30

40

50

60

70

80

90

100

PSO NPSO


4

6

8

10

15

20

25

30

50

Inferences


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The avoidance of premature convergence at local optimalpoints tend to enhance the results

The selection of local best particles based on neighbors

(lbest) rather than particles own best (pbest) enhances

the accuracy of the rules mined

Inferences

Mining ARs using Self Adaptive


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Chaotic PSO

A slight variant of the PSO is called inertia-weight PSO, in which a

weight parameter Is added to the velocity equation adopted

where, w is the inertia weight. The variable w plays the role of

balancing the global search and local search.A method of adaptive mutation rate is used

= m x

m x

min

) g/G

where, g is the generation index representing thecurrent number of evolutionary generations, and G is a

redefined maximum number of generations. Here, the

maximal and minimal weights max and min are usually

set to 0.9 and 0.4, respectively.


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Effect of changing w

Dataset

Highest PA achieved within 50 runs of iterations

No weight

(Normal PSO)w = 0.5 w = 0.7

Lenses 87.5 88.09 84.75

Haberman 87.5 96.07 99.80

Car 96.4 99.88 99.84

POP Care 91.6 98.64 97.91

Zoo 83.3 96.88 98.97


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Lenses

0

1020

30

40

50

60

70

80

90

100

5 10 15 25 50

PredictiveAccu

racy

No of generations

Predictive Accuracy CPSO

Predictive Accuracy

Weighted CPSO

Predictive Accuracy Self

Adaptive CPSO


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Habermans Survival

75

80

85

90

95

100

5 10 15 25 50

PredictiveAccu

racy

No of generations

Predictive AccuracyCPSO

Predictive Accuracy

Weighted CPSO


Adaptive CPSO


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Post operative Patient Care

0

1020

30

40

50

60

70

80

90

100

5 10 15 25 50

PredictiveAccuracy

No. of Generations


Predictive Accuracy

Weighted CPSO


Adaptive CPSO


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Zoo

82

84

86

88

90

92

94

96

98

100

5 10 15 25 50

PredictiveAccu

racy

No. of Generations


Predictive Accuracy

Weighted CPSO


Adaptive CPSO


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Car Evaluation

98.2

98.4

98.6

98.8

99

99.299.4

99.6

99.8

100

5 10 15 25 50

PredictiveAccu

racy

No of generations

Predictive Accuracy CPSO

Predictive Accuracy

Weighted CPSO


Adaptive CPSO

Inferences


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In term of computational efficiency SACPSO is

faster than GA

Setting of appropriate values for the control

parameters involved in these heuristics methods

is the key point to success in these methods

Inferences

Mining AR using Hybrid GA/PSO


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x


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When Genetic algorithm used for mining association

rules Improvement in predictive accuracy achieved

Particle swarm optimization when adopted for mining

association rules produces results closer to GA but with

minimum execution time

The premature convergence being the major drawback

of PSO was handled by introducing inertia weights,chaotic maps, neighborhood selection adaptive inertia

weight

Papers Published


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K.Indira, Dr.S.Kanmani, Framework for Comparison of Association Rule

Mining Using Genetic Algorithm, In : International Conference OnComputers, Communication & Intelligence , 2010.

K.Indira, Dr.S.Kanmani, Mining Association Rules Using Genetic Algorithm:

The role of Estimation Parameters , In : International conference on

advances in computing and communications, Communication in Computer

and Information Science, Springer LNCS,Volume 190, Part 8, 639-648, 2011

K.Indira, Dr. S. Kanmani , Gaurav Sethia.D, Kumaran.S, Prabhakar.J , Rule

Acquisition in Data Mining Using a Self Adaptive Genetic Algorithm, In :

First International conference on Computer Science and Information

Technology, Communication in Computer and Information Science, SpringerLNCS Volume 204, Part 1, 171-178, 2011.

K.Indira, Dr. S.Kanmani, Prasanth, Harish, Jeeva, Population Based Search

Methods in Mining Association Rules , In : Third International Conference

on Advances in Communication, Network, and Computing CNC 2012,

LNICST pp. 255261, 2012.

Conferences

J l


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Journal

K.Indira, Dr. S.Kanmani, Performance Analysis of Genetic Algorithm for

Mining Association Rules, IJCSI International Journal of Computer Science

Issues, Vol. 9, Issue 2, No 1, 368-376, March 2012

K.Indira, Dr. S.Kanmani, Rule Acquisition using Genetic Algorithm,

accepted for publication in Journal of Computing

K.Indira, Dr. S.Kanmani, Enhancing Particle Swarm optimization using

chaotic operators for Association Rule Mining, communicated to

International Journal of Computer Science and Techniques

K.Indira, Dr. S.Kanmani, AssociationRule Mining by Dynamic Neighborhood

Selection in Particle Swarm Optimization, communicated to world science

Publications

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