A Rough Set based Gene Expression Clustering Algorithm

Emilyn, J. Jeba; Ramar, K.

doi:10.3844/jcssp.2011.986.990

Cited by 4 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process repeats until all the centroids have stopped adjusting. [7] D. Results, Visualization and Summary…”

Section: Data Clusteringmentioning

confidence: 98%

“…A Rough Set based Gene Expression Clustering Algorithm was used in analyzing colon cancer dataset in [7] by Emilyn and Ramar. And in [4], Bhuvaneswari and Brintha used a type 2 fuzzy logic approach in converting microarray data into fuzzy terms.…”

Section: Review Of Related Literaturementioning

confidence: 99%

“…Another problem is the difficulty in characterizing the genes of each sample [2]. Clustering analysis of gene expression data aids in the mentioned problems [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Microarray data clustering and visualization tool using self-organizing maps

Marasigan

Dionisio

Solano

2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

View full text Add to dashboard Cite

Microarray is one of the technologies used in the interdisciplinary science of BioInformatics. Its primary objective is to discover biological knowledge among genes through their expressions. Gene expressions usually come in large and multidimensional data which makes computational and statistical analyses necessary. Clustering of microarray data is one of these. Grouping similar genes together unfolds relationships of the biological properties of the genes under specific condition and, if supported by visualization, serves as good decision support for researchers. MaSOM is a software that uses Self-Organizing Maps, an Artificial Neural Network suitable both for clustering and for visualization. This tool can be used to analyze large data set by preprocessing, clustering, and visualizing two-color cDNA microarray data. It can therefore aid microarray researchers and practitioners in determining the initial properties of the data they study before proceeding to their actual experimentation onto their data.

show abstract

“…This process repeats until all the centroids have stopped adjusting. [7] D. Results, Visualization and Summary…”

Section: Data Clusteringmentioning

confidence: 98%

Section: Review Of Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

Microarray data clustering and visualization tool using self-organizing maps

Marasigan

Dionisio

Solano

2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

View full text Add to dashboard Cite

show abstract

“…The main goal in the analysis of large and heterogeneous gene expression datasets was to identify groups of genes that get expressed in a set of experimental conditions. J. Jeba Emilyn and K. Ramar [19] have proposed an intelligent clustering algorithm that is based on the frame work of rough sets. The main aim of their work was to develop a clustering algorithm that would successfully indentify gene patterns.…”

Section: Related Workmentioning

confidence: 99%

Rough Set Theory and Fuzzy Logic Based Warehousing of Heterogeneous Clinical Databases

Kumar¹

2017

Int. J. Unc. Fuzz. Knowl. Based Syst.

View full text Add to dashboard Cite

Large amounts of data about the patients with their medical conditions are presented in the Medical databases. Analyzing all these databases is one of the difficult tasks in the medical environment. In order to warehouse all these databases and to analyze the patient’s condition, we need an efficient data mining technique. In this paper, an efficient data mining technique for warehousing clinical databases using Rough Set Theory (RST) and Fuzzy Logic is proposed. Our proposed methodology contains two phases – (i) Clustering and (ii) Classification. In the first phase, Rough Set Theory is used for clustering. Clustering is one of the data mining techniques for warehousing the heterogeneous data bases. Clustering technique is used to group data that have similar characteristics in the same cluster and also to group the data that have dissimilar characteristics with other clusters. After clustering the data, similar objects will be clustered in one cluster and the dissimilar objects will be clustered under another cluster. The RST can be reduced the complexity. Then in the second phase, these clusters are classified using Fuzzy Logic. Normally, Classification with Fuzzy Logic is generated more number of rules. Since the RST is utilized in our work, the classification using Fuzzy can be done with less amount of complexity. The proposed approach is evaluated using various clinical related databases from heart disease datasets – Cleveland, Switzerland and Hungarian. The performance analysis is based on Sensitivity, Specificity and Accuracy with different cluster numbers. The experimentation results show that our proposed methodology provides better accuracy result.

show abstract