A comparison study of clustering algorithms in grouping microarray data

What is it about?

Clustering is the process of grouping together related data elements into a collection that is distinct from other groups. Alternatively, it is the clustering of data points based on a similarity metric, such distance. By combining related elements or variables, it seeks to reduce the quantity of data while still obtaining meaningful information. Clustering, a kind of unsupervised classification, is the act of generating classes from data without being aware of the labels for the classes. This method is widely used in a number of academic disciplines, including bioinformatics, machine learning, data mining, image analysis, and pattern recognition. The phrase "classification" refers to a process of allocating data items to several classes. Unsupervised classifications must be learned from the data since they are unknowable a priori.

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Photo by Alejo Reinoso on Unsplash

Photo by Alejo Reinoso on Unsplash

Why is it important?

Finding groupings of genes that share co-expression in various settings through the analysis of microarray data is possible through the use of clustering. This study’s primary goal is to compare the effectiveness of various clustering methods while grouping microarray data both with and without outliers.

Perspectives