Supervised and Unsupervised Learning for Genetic Expression

Attribute clustering is one of the unsupervised data mining applications which have been previously used to identify statistical dependence between subsets of variables. Again clustering techniques are important in data mining methods for exploring natural structure and identifying interesting patterns in original data, also it is proved to be helpful in finding co-expressed samples. In this book, the rough set theory (RST) has been used for attribute clustering. RST is a theory adopted to deal with rough and unsure knowledge, which analyzes the clusters and finds the data principles when previous knowledge is not available. In this concern, after implementing the rough set based attribute clustering method on a real life dataset, those are classified using some of the traditional classification techniques.