Kushwaha, Shreeram (2013) Multiobjective optimization of cluster measures in Microarray Cancer data using Genetic Algorithm Based Fuzzy Clustering. BTech thesis.
The field of biological and biomedical research has been changed rapidly with the invention of microarray technology, which facilitates simultaneously monitoring of large number of genes across different experimental conditions. In this report a multi objective genetic algorithm technique called Non-Dominated Sorting Genetic Algorithm (NSGA) - II based approach has been proposed for fuzzy clustering of microarray cancer expression dataset that encodes the cluster modes and simultaneously optimizes the two factors called fuzzy compactness and fuzzy separation of the clusters. The multiobjective technique produces a set of non-dominated solutions. This approach identifies the solution i.e. the individual chromosome which gives the optimal value of the parameters.
|Item Type:||Thesis (BTech)|
|Uncontrolled Keywords:||Fuzzy Clustering; Microarray expression data; Multiobjective Optimization|
|Subjects:||Engineering and Technology > Computer and Information Science > Data Mining|
|Divisions:||Engineering and Technology > Department of Computer Science|
|Deposited By:||Hemanta Biswal|
|Deposited On:||19 Dec 2013 10:44|
|Last Modified:||19 Dec 2013 10:44|
|Supervisor(s):||Rath, S K|
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