Bio-inspired technology systems and data analysis methods for classification and subsequent decision making intended for automated systems. (2016)

• Record Type: Journal Article
• Authors: Mishra, D.; Rath, A.; Zhang, D.; Mahalik, N.P.
• Is Part Of: International journal of computational vision and robotics. Volume 6:Number 4(2016)
• Page Start: 381
• Page End: 398
• Publication Date: 2016
• Subjects: automation -- safety -- control -- bio-inspired computation -- biological data analysis -- decision making -- artificial intelligence -- data mining -- clustering algorithms -- gene expression data -- rough PCA: principal component analysis -- R-PCA -- bioinformatics; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

The objective of this research is to study various types of recently developed data mining algorithm and contribute a new method. We have reviewed biological data analysis methods, which will lead us to foresee if the set of proposed algorithms can help in analysing data in areas such as automation and safety. In this research we have addressed clustering. By establishing some problems, we identified the solutions through developed algorithm. The algorithms are developed around high dimensional gene expression dataset. We have shown that the proposed rough principal component analysis (R-PCA) scheme is capable of handling the high dimensionality. We have also reviewed and formulated a validation index for the measurement of quality. Our analysis considers GA framework. The paper presents introduction and review, methods, results, discussion, and future work in appropriate order.