Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Model for Information Retrieval Agent System Based on Keywords Distribution
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Multi-objective Classification Rule Mining Using Gene Expression Programming
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 02
Homeland Security Data Mining Using Social Network Analysis
EuroISI '08 Proceedings of the 1st European Conference on Intelligence and Security Informatics
Scalable Rule-Based Gene Expression Data Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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The DNA mciroarray gene data is in the expression levels of thousands of genes for a small amount of samples. From the microarray gene data, the process of extracting the required knowledge remains an open challenge. Acquiring knowledge is the intricacy in such types of gene data, though number of researches is arising in order to acquire information from these gene data. In order to retrieve the required information, gene classification is vital; however, the task is complex because of the data characteristics, high dimensionality and smaller sample size. Initially, the dimensionality diminution process is carried out in order to shrink the microarray data without losing information with the aid of LPP and PCA techniques and utilized for information retrieval. In this paper, we propose an effective gene retrieval technique based on LPP and PCA called LPCA. The technique like LPP and PCA is chosen for the dimensionality reduction for efficient retrieval of microarray gene data. An application of microarray gene data is included with classification by SVM. SVM is trained by the dimensionality reduced gene data for effective classification. A comparative study is made with these dimensionality reduction techniques.