Anatomization of Clustering of Iris Data Implemented using Genetic Algorithm

Volume 10  Issue 2    2016

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Author(s): Sajid Saleem, Syed Nazeer Alam
Abstract Genetic algorithms offer an approach to optimize the data. Genetics consist of genes which provide a blueprint to basic building block of life. Genes combined together to form a chromosome. Organisms share their genes, such as children share the genes of parents. This is called crossover. Permanent change can be done to the genes by means of mutation. Mutation is the process where a gene or a chromosome is permanently changed. Clustering algorithms attempt to revamp the positioning of like objects into homogeneous classes and objects. This paper describes the concept of genetic algorithm and clustering on Fishers IRIS to illustrate the concept of obtaining fittest gene from a given set of chromosomes. We employed K-means technique for clustering. We have generated chromosomes using IRIS data. In order to randomize and get unique chromosomes we applied crossover and mutation methodology repeatedly in cyclic manner to obtain optimized chromosomes. We tested each chromosome with a fitness function and seek optimized genes from the tested sample. Clustering applications are in various fields including marketing, biology, etc and genetic algorithms provide stochastic optimization techniques. Merging both technique help in efficient classification of biological samples, marketing samples, etc.
Keywords Clustering, Genetic Algorithm, Fitness, Chromosome.
Year 2016
Volume 10
Issue 2
Type Research paper, manuscript, article
Journal Name Journal of Information & Communication Technology
Publisher Name ILMA University
Jel Classification -
DOI -
ISSN no (E, Electronic) 2075-7239
ISSN no (P, Print) 2415-0169
Country Pakistan
City Karachi
Institution Type University
Journal Type Open Access
Manuscript Processing Blind Peer Reviewed
Format PDF
Paper Link https://jict.ilmauniversity.edu.pk/journal/jict/10.2/6.pdf
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