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Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Discretization plays a major role as a data preprocessing technique used in machine learning and data mining. Recent studies have focused on multivariate discretization that considers relations among attributes. The general goal of this method is to obtain the discrete data, which preserves most of the semantics exhibited by original continuous data. However, many techniques...

Generating descriptive model for student dropout: a review of clustering approach

authors read and approved the final manuscript. Acknowledgements Natthakan IamOn—main contributor. 1. Romero C , Ventura S ( 2010 ) Educational data mining: a review of the state‑ of‑the ‑art . IEEE

Comparative study of matrix refinement approaches for ensemble clustering

Cluster ensembles or consensus clusterings have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across various sets of data. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique. Since founded, different research areas have emerged with...

LCE: a link-based cluster ensemble method for improved gene expression data analysis

Motivation: It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by...