Learning an un-supervised – Clustering algorithm Monte Carlo over Consensus Clustering for Genomic Data for Tumor Identification
Tejal Upadhyay1, Samir Patel2
1Prof Tejal Upadhyay, Assistant Professor, Department of Engineering, Gujarat University, Ahmadabad, India.
2Dr Samir Patel, Assistant Professor, Department of Computer Science and Engineering, Pandit Deendayal Petrolium University, Raisan, Gandhinagar, Gujarat, India.

Manuscript received on November 22, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on November 30, 2019. | PP: 2751-2756 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7370118419/2019©BEIESP | DOI: 10.35940/ijrte.D7370.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Clustering involves the grouping of similar objects into a set known as cluster. Objects in one cluster are likely to be different when compared to objects grouped under another cluster. Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product. Subgroup classification is a basic task in high-throughput genomic data analysis, especially for gene expression and methylation data analysis. Mostly, unsupervised clustering methods are applied to predict new subgroups or test the consistency with known annotations. To get a stable classification of subgroups, consensus clustering is always performed. It clusters repeatedly with a randomly sampled subset of data and summarizes the robustness of the clustering. When faced with significant uncertainty in the process of making a forecast or estimation, the Monte Carlo Simulation might prove to be a better solution. Monte Carlo3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overfitting and can reject the null hypothesis when only one cluster is there.
Keywords: Consensus clustering, Monte Carlo Reference based Clustering, Genomic data, Supervised Learning Clustering.
Scope of the Article: Clustering.