DC Meet Second

8/11/2019 DC Meet Second

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Presented By

K.Indira

Under the Guidance of

Dr. S. Kanmani

Professor & HeadDepartment of Information Technology

Pondicherry Engineering College

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Optimal Distributive Genetic Algorithm for

Mining Association Rules.


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2

To propose and implement Self adaptive

Distributive Genetic Algorithm for Association

Rule Mining.

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 analysis is the discovery of what are

commonly called association rules.

It studies the frequency of items occurring together in

transactional databases

Association rule mining provides valuable

information in assessing significant correlations.

ASSOCIATION ANALYSIS

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Association Rules

Find all the rulesX Ywithminimum 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%)

Sugar Milk (60%, 75%)


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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.

Genetic algorithms use biologically inspiredtechniques such as genetic inheritance, natural

selection, mutation, and sexual reproduction

(recombination, or crossover).

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START

GENERATE INITIALPOPULATION

EVALUATION

GENETIC OPERATORS(CROSSOVER, MUTATION)

SELECTION

STOP

TERMINALCONDITION

No

Yes

Conceptual Algorithm


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I.IV MutationLocus point of mutation

Weight factor taken into consideration for deciding locus pointDynamic mutation pointMutation 1 and Mutation 2 generated

I.V Fitness ThresholdDynamically setTP,TN, FP,FN criteria consideredStrength of implication taken into considerationSustainability index, creditable index and inclusive index considered

Real values of Confidence and Support derived and appliedPredictability and Comprehensibility factors considered.

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Existing Work Contd..


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2. MethodologyCrossover replaced by symbiotic combination

Rules selection performed by user thereby seeding populationto next generationSearching for rules in K- itemset instead of whole databaseDistributed GA performedDynamic immune evolution and biometric mechanismintroduced

3. Application Areas

4. Evaluation Parameters.Population Size

Chromosome LengthMutation ProbabilityCrossover probabilityFitness thresholdSupport and Confidence Factor

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Existing Work Contd..


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Open Issues

Mining Rules with non fixed consequent.

Combined with other methods for multi-relation data.

Elimination of redundant rules.

Fixing optimum values for parameters.

Enhance self addictiveness.

Rule selection made dependent on other classes.

Algorithm could be improved to generate further

simpler rules.

Test on different domain.Complexity prediction by using Distributed Computing.

Scalability.

Unsupervised Learning.

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Proposed Work

To implement self adaptive Genetic Algorithm forAssociation Rule Mining with optimal accuracy.

By Iterative Approach to increase the number of rules

extracted in each iteration, as a way to decrease thetime for learning.

To propose the Self Adaptive GA in Distributive

Environment.

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Self Adaptive GA

SELFADAPTIVE


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Work Done So Far

Literature survey performed on genetic algorithm and

comparative study based on other methods done .

Analysis on Existing Rule mining method : Apriori done

Basic Genetic Algorithm for optimizing function coded

in Java.

Proposed a comparison framework on Genetic

algorithm in Association Rule Mining.

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Work to be done

Implementing association rule mining with self

adaptive Genetic Algorithm on medical dataset.

Test the same algorithm on other dataset and

compare with existing methods.

Optimize result with GA parameters.

Survey on Distributed Algorithm.

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Execution Plan for Next Six Months

July-August Implementing an existing paper

August - Testing the code with Medical data set and

perform comparative study

September - Alter the code for other datasets and compare

the result obtained

October - Make alteration in GA factors in code & evaluate

the results

November - Feasibility study on generated code to obtain

Decembers optimum result.

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Papers Published

Paper titled Framework for Comparison of Association Rule

Mining Using Genetic Algorithm has been selected for The

International Conference On Computers, Communication &

Intelligence at VCET, Madurai.

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

Coded, International Conference on Biomedical Engineering and

computer Science , Page(s): 1 - 4 , 2010

Chuan-Kang Ting, Wei-Ming Zeng, Tzu-Chieh Lin, Linkage Discovery

through 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 Genetic

Algorithm Based on Image Databases for Mining Association Rules, 6th

IEEE/ACIS International Conference on Computer and Information Science,

Page(s): 977980, 2007

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

Rule Extraction,. Eighth International Conference on Hybrid Intelligent

Systems, HIS '08. Page(s): 531536, 2008

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Mansoori, E.G., Zolghadri, M.J., Katebi, S.D., SGERD: A Steady-State

Genetic Algorithm for Extracting Fuzzy Classification Rules From

Data, IEEE Transactions on Fuzzy Systems, Volume: 16 , Issue: 4 ,

Page(s): 10611071, 2008..

Xiaoyuan Zhu, Yongquan Yu, Xueyan Guo, Genetic Algorithm Based on

Evolution Strategy and the Application in Data Mining, First

International 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 Based

on 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 Genetic

Algorithm 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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hank You

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