An Effective Triclustering Algorithm for Mining Real Datasets: Review Paper

A large number of clustering approaches have been proposed for the analysis of synthetic datasets obtained. However, the results of the application of standard clustering methods are limited. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions has been proposed to date. This simultaneous clustering, usually designated by biclustering, seeks to find sub-matrices, that is subgroups of rows and subgroups of columns. Most recently for the clustering of large real dataset the triclustering technique is implemented. Triclusters are constructed or discovered from two datasets by selecting a subset of features from each dataset and one shared subset of rows from amongst all the rows. This type of algorithms has also been proposed and used in other fields, such as information retrieval and data mining. 1