Data Mining Using Genetic Algorithm

8/3/2019 Data Mining Using Genetic Algorithm

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Contents

What Is Data Mining?

Architecture of Typical Data Mining System

Biological Terminologies

What is Genetic Algorithm(GA)?

Basic Principles of GA Why Data Mining using Genetic Algorithm?

Functions of Genetic Algorithm

Pseudo Code of GA

Applications of GA

Advantages and Disadvantages

The ToolMATLAB

Conclusion & Future Work

References

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What Is Data Mining?

Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and

potentially useful)patterns or knowledge from huge amount of data[1].

Data mining: a misnomer?

Alternative names

Knowledge discovery (mining) in databases (KDD), knowledge

extraction, data/pattern analysis, data archeology, data dredging,

information harvesting, business intelligence, etc.

Is everything data mining?

Simple search and query processing

Expert systems

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Architecture: Typical Data Mining System

data cleaning, integration, and selection

Database or Data WarehouseServer

Data Mining Engine

Pattern Evaluation

Graphical User Interface

Knowl

edge-

Base

DatabaseData

Warehouse

World-Wide

Web

Other Info

Repositories [1]

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Biological Terminologies[2]

Gene - Each gene encodes a particular protein. Basically can be said, that

each gene encodes a trait, for example color of eyes.

Chromosomes - A chromosome consist ofgenes, blocks of DNA.

Chromosomes are strings of DNA and serves as a model for the whole

organism.

Alleles - Possible settings for a trait (e.g. blue, brown) are called alleles.

Locus - Each gene has its own position in the chromosome. This position is

called locus.

Genome - Complete set of genetic material (all chromosomes) is calledGenome.

Genotype - Particular set of genes in genome is called Genotype.

PhenotypeThe genotype contains the information required to construct an

organism which is referred to as the phenotype. 5


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Genetic Algorithm(GA) GA was developed by John Holland in 1970.

They are based on the genetic processes of biologicalorganisms.

Over many generations, natural populations evolveaccording to the principles ofnatural selection andsurvival of the fittest, first clearly stated by CharlesDarwin in the Origin of Species.

GAs are adaptive method which may be used to solve searchand optimization problems.

After a number of new generations built with the help of thedescribed mechanisms one obtains a solution that cannot be

improved any further. This solution is taken as a final one. 6


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Basic Principles of GA

Coding

Fitness function

Reproduction Selection

Crossover

Mutation Convergence

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Coding

Before a GA can be run, a suitable coding(or representation)

for the problem must be devised.

It is assumed that a potential solution to a problem may berepresented as a set of parameters (for example, the

dimensions of the beams in a bridge design).

For example, if our problem is to maximize a function ofthree variables, F(x, y, z), we might represent each variable by

a 10-bit binary number. Our chromosome would therefore

contain three genes, and consist of 30 binary digits.

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Fitness Function

A fitness function must be devised for each problem to be

solved.

Given a particular chromosome, the fitness function returns asingle numerical fitness or figure of merit.

Which is supposed to be proportional to the utility or

ability of the individual which that chromosome represents.

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Reproduction

During the reproductive phase of the GA, individuals are

selectedfrom the population and recombined, producing

offspring which will comprise the next generation.

Parents are selected randomly from the population using a

scheme which favours the more fit individuals.

Having selected two parents, their chromosomes arerecombined, typically using the mechanisms ofcrossoverand

mutation.

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Example of Crossover & Mutation

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Convergence

Convergence is the progression towards increasing uniformity.

A gene is said to have converged when 95% of the population

share the same value.

The population is said to have converged when all of the geneshave converged.

If the GA has been correctly implemented, the population will

evolve over successive generations so that the fitness of the

best and the average individual in each generation increases

towards the global optimum.

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Why Data Mining using Genetic

AlgorithmThere are more reasons for preference using genetic algorithms-

Its robustness

Ability to work on large and noisy datasets,

GAs perform global search of the solution space in comparison tomost other algorithms that use Greedy approach

Coping well with attribute interaction.

Parallel approaches to genetic algorithms,

the scalability of these algorithms can be achieved. this characteristic is of great importance in data mining.

Moreover, genetic algorithms have high degree of autonomy thatenables discovery of knowledge previously unknown by the user.

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Functions of Genetic Algorithm

The Fitness Function

The fitness score is returned as a result

Parent Selection

Mating Pool Crossover

Likelihood of crossover being applied is typically between 0.6and 1.0.

Mutation

Mutation is applied to each child individually after crossover. Itrandomly alters each gene with a small probability (typically0.001).

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Pseudo Code of GA[3]

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Applications of GA

Domain Application TypesControl gas pipeline, pole balancing, missile evasion, pursuit

Design semiconductor layout, aircraft design, keyboardconfiguration, communication networks

Scheduling manufacturing, facility scheduling, resource allocation

Robotics trajectory planning

Machine Learning designing neural networks, improving classificationalgorithms, classifier systems

Signal Processing filter design

Game Playing poker, checkers, prisoners dilemma

CombinatorialOptimization

set covering, travelling salesman, routing, bin packing,graph colouring and partitioning


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Advantages and Disadvantages

Advantages:

Concept is easy to understand

Modular, separate from application

It doesnt have to know any rules of the problem in advance.

This is very useful for very complex and loosely defined

problem.

With a well defined fitness function and carefully chosen

attributes, genetic algorithm can perform much faster than

other algorithm such as the linear method.

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Conti

Disadvantages:- The definition of the fitness function can be very complicated

sometime.

The fitness function may affect the performance of the process

significantly if the complexity of the fitness function increase.

It is because the fitness function is used to compare every

element in the sample population to every data in the training

data set.

Sometimes an acceptable solution cannot be derived even aftercountless iteration if the genetic operators are wrongly chosen.

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The ToolMATLAB[4]

MATLABMatrix Laboratory

MATLAB is a high-performance language for technical

computing. It integrates computation, visualization and

programming in an easy-to-use environment where problems

and solutions are expressed in familiar mathematical notation.

Simulink -

Simulink is an interactive environment for modeling,simulating, and analyzing dynamic, multi domain systems. It

lets you build a block diagram, simulate the systems behavior,

evaluate its performance, and refine the design.

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Typical Uses Of Matlab

Math and computation

Algorithm development

Data acquisition

Modeling, simulation, and prototyping Data analysis, exploration, and visualization

Scientific and engineering graphics

Application development, including graphical user interface

building

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References

1. Jiawei Han and Micheline Kamber, Data Mining: Concepts

and Techniques, 2006

2. http://www.obitko.com/tutorials/genetic-algorithms/index.php

3. David Beasley et. al. (1993). An Overview of Genetic

Algorithms: Part 1, Fundamentals, University Computing,

vol.15 (2), pp. 58-69.

4. Learning MATLAB, COPYRIGHT 1984 - 2004 by The

MathWorks, Inc.

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Thank You

any

question or suggestion

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Data Mining Using Genetic Algorithm