Dcmeet Second v2

8/4/2019 Dcmeet Second v2

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Presented By

K.Indira

Under the Guidance of

Dr. S. Kanmani,

Professor,Department of Information Technology,

Pondicherry Engineering College.

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Mining Association Rules using OptimalGenetic Algorithm & Quantum Swarm

intelligent PSO.


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Objective.

Introduction.Data Mining.Association Analysis.Limitations of the existing system.GA and PSO An Introduction.

Existing Work.Based on GA.Based on PSO.

Work Done So far.

Proposed Work.

Papers Published.

References.

Contents

Execution Plan.


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To Propose an efficient methodology formining of ARs using Optimal Genetic

Algorithm & Quantum Swarm intelligentPSO

Objective


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Extraction of interesting information orpatterns from data in large databases is knownas data mining.

Data Mining

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Association Rules

Find all the rulesXYwithminimum support andconfidence

Support, s, probability that a

transaction contains X

Y Confidence, c, conditional

probability that a transactionhaving X also contains Y

Let minsup = 50%, minconf = 50%

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

Customer

buys sugar

Customer

buys both

Customer

buys milk

Nuts, Eggs, Bread40

Nuts, Coffee, Sugar , Eggs, Bread50

Milk, Sugar, Eggs30

Milk, Coffee, Sugar20

Milk, Nuts, Sugar10

Items boughtTid

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


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Apriori, 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.

Limitations of Existing System


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GA and PSO An Introduction

Evolutionary algorithms provide robust andefficient approach in exploring large search space.

A Genetic Algorithm (GA) is a procedure used to

find approximate solutions to search problemsthrough the application of the principles ofevolutionary biology.

PSOs mechanism is inspired by the social andcooperative behavior displayed by various specieslike birds, fish etc including human beings.

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Existing Work

Mining ARs Based on Genetic Algorithm

Efficient Distributed Genetic Algorithm done by spatialpartitioning of the population into several semi-isolated nodes,each evolving in parallel and possibly exploring different regionsof the search space.

Genetic algorithm without taking the minimum support and

confidence into account. Extracts the best rules that have bestcorrelation between support and confidence

Improved niched Pareto genetic algorithm(INPGA), selects theaccurate candidates and also saves selection time with combining

BNPGA and SDNPGA

GRA with a new operator, called guided mutation is introduced.GRA considers the correlation coefficient between nodes in eachindividual of GRA.


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Mining ARs Based on Particle Swarm Optimization

Existing Work contd..

A novel algorithm for association rule mining in order to improve

computational efficiency as well as to automatically determinesuitable threshold values.

The algorithm operates at three evolution levels where an adaptiveinertia weight is presented. The safety distance is introduced to move

the particle through its current position, and the proximity index. Self-adaptive method to adjust the inertia weight of the velocity update

rule based on the empirical values and negative feedback technique isintroduced ,which relieve the burden of specifying the parametersvalues.

Combines Particle Swarm Optimization (PSO) and Genetic Algorithms(GAs) using fuzzy logic to integrate the results of both methods and forparameters tuning. The new optimization method combines theadvantages of PSO and GA to give us an improved FPSO + FGA hybrid

approach.


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Work Done so Far

Association Rule Mining was carried out using theGenetic Algorithm in Matlab 2008a.

Mining of Association rule was carried out using selfAdaptive Genetic algorithm using Java.

The GA Parameters were varied and the results wererecorded for each cases.


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Mining ARs using GA in Matlab 2008a.

MethodologySelection : Tournament

Crossover Probability : Fixed ( Tested with 3 values)

Mutation Probability : No Mutation

Fitness Function :

Dataset : Lenses, Iris, Haberman fromUCI Irvine repository.

Population : Fixed ( Tested with 3 values)


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Flow chart of the GA


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Results Analysis

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

%

No. ofGenerations

Accuracy%

No. ofGenerations

Accuracy%

No. ofGenerations

Lenses 75 7 82 12 95 17Haberman 71 114 68 88 64 70Iris 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. ofGen

Accuracy%

No. ofGen.

Accuracy%

No. ofGen.

Accuracy%

No. ofGen

Lenses 22 20 49 11 70 21 95 18Haberman 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 OverPc = .25 Pc = .5 Pc = .75

Accuracy % No. ofGenerations



Lenses 95 8 95 16 95 13Haberman 69 77 71 83 70 80Iris 84 45 86 51 87 55

Dataset No. of

Instances

No. of

attributes

Populatio

n Size

Minimum

Support

Minimum

confidence

Crossover

rate

Accuracy

in %

Lenses 24 4 36 0.2 0.9 0.25 95Haberman 306 3 306 0.9 0.2 0.5 71Iris 150 5 225 0.2 0.9 0.75 87

Comparison of the optimum value ofParameters for maximum Accuracy achieved

Comparison based on variation in Crossover Probability


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Values of minimum support, minimum confidence and

population size decides upon the accuracy of the systemthan 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.

The size of the dataset and relationship between

attributes in data contributes to the setting up of the

parameters.

Inferences


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Mining ARs using Self Adaptive GA inJava.

MethodologySelection : Roulette Wheel

Crossover Probability : Fixed ( Tested with 3 values)

Mutation Probability : Self Adaptive

Fitness Function :

Dataset : Lenses, Iris, Car fromUCI Irvine repository.

Population : Fixed ( Tested with 3 values)


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Procedure SAGA

BeginInitialize 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

SELFADAPTIVE

l l


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Dataset Traditional GA Self Adaptive GAAccuracy No. of

GenerationsAccuracy No. of Generations

Lenses 75 38 87.5 35

Haberman 52 36 68 28

CarEvaluation

85 29 96 21

Dataset Traditional GA Self Adaptive GAAccuracy No. of

GenerationsAccuracy No. of

GenerationsLenses 50 35 87.5 35

Haberman 36 38 68 28

CarEvaluation 74 36 96 21

ACCURACY COMPARISON BETWEEN GA AND SAGA WHEN PARAMETERS AREACCORDING TO TERMINTAION OF SAGA

ACCURACY COMPARISON BETWEEN GA AND SAGA WHEN PARAMETERS ARE IDEALFOR TRADITIONAL GA

Results Analysis


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Inferences

Better accuracy.

Better convergence.

Self Adaptive GA gives better accuracy than

Traditional GA.


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Proposed Work

1. To implement a Distributive niched Pareto memetic

Algorithm for Rule Mining.

2. To propose a association rule mining algorithm basedon Chaotic PSO and swarm intelligence.

3. Propose a Particle swarm optimization rule miningmethodology combined with quantum computing andquantum differential evolution


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Obtains the comparison set S from clustering based samples.

For any two candidates and comparison set S, if one candidate is

dominated and the other not, the candidate non-dominated is

selected, Exit.

If two candidates (cd_1 and cd_2) compute the number of samples

in two niches, count1 and count2. Ifcount1=0, cd_1 is selected and if count2=0, cd_2 is selected, Exit.

If count1-count2>delta or count2-count1>delta, then selects

cd_2 or cd_1, Exit..

If abs(count1-count2)sd2, cd_1 is selected, otherwise, cd_2 is selected.

Exit

Niched Pareto Selection Algorithm


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Distributed Model

GA1subpopulation

GA2subpopulation

GA3subpopulation

GA4subpopulation Full Dataset

RulesGenerated

RulesGenerated

RulesGenerated

RulesGenerated

Concept

Description

Association Rule mining Algorithm based on Chaotic


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Based onchaotic maps

Association Rule mining Algorithm based on ChaoticPSO and Swarm intelligence.

Swarm IntelligenceConcept

E ti Pl


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Execution Plan

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July : Niched Pareto Sampling based Selection.Implementing GA for Local intensity Search.

August : Distributed Methodology Implementation.Preparing the Above work as a paper.

September& : Particle Swarm Optimization basedOctober Rule Mining to be implemented.

November : Chaotic PSO & Swarm intelligence based PSOfor Mining ARs to be implemented.Documenting the same into paper.

December& : Study on Quantum computing and

January differential Evolution concepts.

P P bli h d


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Papers Published

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Paper titled Framework for Comparison of Association RuleMining Using Genetic Algorithm has been presented in the

International Conference On Computers, Communication &Intelligence at VCET, 2010.

Paper titled Mining Association Rules Using GeneticAlgorithm: The role of Estimation Parameters has beenSelected for presentation in the International conference onadvances in computing and communications ,2011. To bepublished in Springer LNCS (CCIS) series.

Paper titled Rule Acquisition in Data Mining Using a SelfAdaptive Genetic Algorithm has been Selected for

presentation in the First International conference on ComputerScience and Information Technology (CCSEIT-2011) , To bepublished in Springer LNCS (CCIS) series.

R f


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References Jing Li, Han Rui-feng, A Self-Adaptive Genetic Algorithm Based On Real-

Coded, International Conference on Biomedical Engineering andcomputer Science , Page(s): 1 - 4 , 2010

Chuan-Kang Ting, Wei-Ming Zeng, Tzu- Chieh Lin, Linkage Discoverythrough Data Mining, IEEE Magazine on Computational Intelligence,

Volume 5, February 2010.

Caises, Y., Leyva, E., Gonzalez, A., Perez, R., An extension of the Genetic

Iterative Approach for Learning Rule Subsets , 4th International Workshopon Genetic and Evolutionary Fuzzy Systems, Page(s): 63 - 67 , 2010

Shangping Dai, Li Gao, Qiang Zhu, Changwu Zhu, A Novel GeneticAlgorithm Based on Image Databases for Mining Association Rules, 6thIEEE/ACIS International Conference on Computer and Information Science,

Page(s): 977 980, 2007

Peregrin, A., Rodriguez, M.A., Efficient Distributed Genetic Algorithm for

Rule Extraction,. Eighth International Conference on Hybrid Intelligent

Systems, HIS '08. Page(s): 531 536, 2008

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Mansoori, E.G., Zolghadri, M.J., Katebi, S.D., SGERD: A Steady-StateGenetic Algorithm for Extracting Fuzzy Classification Rules From

Data, IEEE Transactions on Fuzzy Systems, Volume: 16 , Issue: 4 ,Page(s): 1061 1071, 2008..

Xiaoyuan Zhu, Yongquan Yu, Xueyan Guo, Genetic Algorithm Based onEvolution Strategy and the Application in Data Mining, FirstInternational Workshop on Education Technology and Computer Science,ETCS '09, Volume: 1 , Page(s): 848852, 2009

Hong Guo, Ya Zhou, An Algorithm for Mining Association Rules Basedon Improved Genetic Algorithm and its Application, 3rd International

Conference on Genetic and Evolutionary Computing, WGEC '09, Page(s):

117120, 2009

Genxiang Zhang, Haishan Chen, Immune Optimization Based GeneticAlgorithm for Incremental Association Rules Mining, International

Conference on Artificial Intelligence and Computational Intelligence, AICI'09, Volume: 4, Page(s): 341345, 2009

References Contd..

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Maria J. Del Jesus, Jose A. Gamez, Pedro Gonzalez, Jose M. Puerta,On the Discovery of Association Rules by means of Evolutionary

Algorithms, from Advanced Review of John Wiley & Sons , Inc. 2011 Junli Lu, Fan Yang, Momo Li, Lizhen Wang, Multi-objective Rule

Discovery Using the Improved Niched Pareto Genetic Algorithm,Third International Conference on Measuring Technology andMechatronics Automation, 2011.

Hamid Reza Qodmanan, Mahdi Nasiri, Behrouz Minaei-Bidgoli,Multi Objective Association Rule Mining with Genetic Algorithmwithout specifying Minimum Support and Minimum Confidence,Expert Systems with Applications 38 (2011) 288298.

Miguel Rodriguez, Diego M. Escalante, Antonio Peregrin, EfficientDistributed Genetic Algorithm for Rule Extraction, Applied SoftComputing 11 (2011) 733743.

J.H. Ang, K.C. Tan , A.A. Mamun, An Evolutionary MemeticAlgorithm for Rule Extraction, Expert Systems with Applications 37

(2010) 13021315.

References

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R.J. Kuo, C.M. Chao, Y.T. Chiu, Application of particle swarm optimizationto association rule mining, Applied Soft Computing 11 (2011) 326336.

Bilal Alatas , Erhan Akin, Multi-objective rule mining using a chaoticparticle swarm optimization algorithm, Knowledge-Based Systems 22(2009) 455460.

Mourad Ykhlef, A Quantum Swarm Evolutionary Algorithm for miningassociation rules in large databases, Journal of King Saud University Computer and Information Sciences (2011) 23, 16.

Haijun Su, Yupu Yang, Liang Zhao, Classification rule discovery withDE/QDE algorithm, Expert Systems with Applications 37 (2010) 12161222.

Jing Li, Han Rui-feng, ASelf-Adaptive Genetic Algorithm Based On Real-Coded, International Conference on Biomedical Engineering andcomputer Science , Page(s): 1 - 4 , 2010

Chuan-Kang Ting, Wei-Ming Zeng, Tzu- Chieh Lin, Linkage Discoverythrough Data Mining, IEEE Magazine on Computational Intelligence,

Volume 5, February 2010.

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References Contd..


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Caises, Y., Leyva, E., Gonzalez, A., Perez, R., An extension of theGenetic Iterative Approach for Learning Rule Subsets , 4thInternational Workshop on Genetic and Evolutionary Fuzzy Systems,Page(s): 63 - 67 , 2010

Xiaoyuan Zhu, Yongquan Yu, Xueyan Guo, Genetic Algorithm Based onEvolution Strategy and the Application in Data Mining, FirstInternational Workshop on Education Technology and ComputerScience, ETCS '09, Volume: 1 , Page(s): 848 852, 2009

References Contd..


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References Miguel Rodriguez, Diego M. Escalante, Antonio Peregrin, Efficient

Distributed Genetic Algorithm for Rule extraction, Applied Soft

Computing 11 (2011) 733743.

Hamid Reza Qodmanan , Mahdi Nasiri, Behrouz Minaei-Bidgoli,Multi objective association rule mining with genetic algorithmwithout specifying minimum support and minimum confidence,

Expert Systems with Applications 38 (2011) 288298.

Junli Lu, Fan Yang, Momo Li1, Lizhen Wang, Multi-objective RuleDiscovery Using the Improved Niched Pareto Genetic Algorithm, 2011Third International Conference on Measuring Technology andMechatronics Automation.

Yan Chen, Shingo Mabu, Kotaro Hirasawa, Genetic relation algorithmwith guided mutation for the large-scale portfolio optimization,Expert Systems with Applications 38 (2011) 33533363.

References


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References

R.J. Kuo, C.M. Chao, Y.T. Chiu, Application of particle swarm

optimization to association rule mining, Applied Soft Computing 11(2011) 326336

Yamina Mohamed Ben Ali, Soft Adaptive Particle Swarm Algorithmfor Large Scale Optimization, IEEE 2010.

Feng Lu, Yanfeng Ge, LiQun Gao, Self-adaptive Particle SwarmOptimization Algorithm for Global Optimization, 2010 SixthInternational Conference on Natural Computation (ICNC 2010)

Fevrier Valdez, Patricia Melin, Oscar Castillo, An improved

evolutionary method with fuzzy logic for combining Particle SwarmOptimization and Genetic Algorithms, Applied Soft Computing 11(2011) 26252632


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Dcmeet Second v2