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Among the soft clustering approaches FCM is most popular.
2.3.8.1 FCM (Fuzzy Class Membership)
This algorithm is mostly applied in microarray data analysis as microarrays are collection of tens of thousands of genes and analysing them concurrently. This uses a membership function upon which a membership matrix is built from the dataset. This is updated at every instance of similarity check with the data points. The degree of membership is given by the weights of the matrix [25] which specifies the data point how similar it is to the mean of a cluster. The membership values ranges from 0 to 1.
2.4 Conclusion
This chapter provides an overview of unsupervised learning algorithms and approaches used in the field of bioinformatics for the exploration of gene expression data. The chapter provide insights about various clustering algorithms used in the field of bioinformatics. These algorithms when applied on gene expression data helps in building gene expression profiling in which co expressed genes are clustered together that exhibits similar cell function, identification of gene homology which aids researchers in drug discovery based on the diseased targets using the microarray analyses. These clustering algorithms also comprehend the genetic data in studying about gene functions, identifying sub types of cells which assist in diseased target identification.
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