"Hierarchical Clustering of Microarray Data based on Empirical Covariance"

Supervised by Dr ND Lawrence

Abstract

The genome revolution has unfolded large amounts of biological data which is required to be reviewed and analysed. The use of Microarray technology is the proof of biology adopting computational methods to summarise the results. This growing dependency is the motivation computer scientists to develop methods that are effective and improve the study of the biological data. One of the most popular methods to analyse the Microarray data is the Hierarchical Clustering. The aim of any method summarising Microarray data is to be able to provide the information to the biologists in a manner which can be understood easily and is presentable. In this project we have reviewed some of the clustering algorithms and attempt to implement the Standard Hierarchical Clustering algorithm. Further we propose a new algorithm which calculates the distance metric based on Empirical covariance and analyse the results from both the implementations. copy the text here. You can use *simple* html, e.g. This is the first paragraph of my great work

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