BibTeX:

@article{IJIRSTV3I1095,
     title={Resolving Gene Expression Data using Multiobjective Optimization Approach},
     author={Sushmita Chakraborty and Toran Verma},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={1},
     pages={205--211},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I1095.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Data mining also known as knowledge discovery in database has been recognized as a promising new area for database research. Studying the patterns hidden in gene expression data helps to understand the functionality of genes. In General, clustering techniques are widely used for identification of partitioning from gene expression data. The proposed work in this paper is about optimizing the data with fuzzy C-Mean clustering algorithm and using multi-objective optimization method i.e. Non-Dominant Sorting Genetic algorithm-2. In the first phase it optimizes the data to reduce the number of comparisons using clustering. Fuzzy C-means algorithm is invoked on the data sets, based on the highest membership values of data points with respect to different clusters, labelled information are extracted . In each case only 10% class labelled information of data points are randomly selected which acts as supervised information. In the Second phase it is implemented with multi-objective genetic algorithm to find fitness function.

ARI-index, Coefficient Entropy index, Fuzzy C-means, Genetic Algorithm, Multiobjective optimization, Partitioning Clustering and non-dominant sorting