Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data

Maji, Pradipta

doi:10.1109/tsmcb.2010.2050684

Cited by 72 publications

(41 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…55 In the proposed method, a new rough hypercuboid based similarity measure is developed to calculate similarity between two miRNAs. Whereas, in ref.…”

Section: Supervised Mirna Clustering Algorithmmentioning

confidence: 99%

“…Whereas, in ref. 55, a fuzzy-rough supervised similarity measure is proposed. However, the fuzzy-rough supervised similarity measure is sensitive to the fuzzy parameter that is used to calculate the similarity between two objects.…”

Section: Supervised Mirna Clustering Algorithmmentioning

confidence: 99%

“…It is developed by integrating the concepts of rough hypercuboid equivalence partition matrix 62,64 and supervised attribute clustering algorithm. 55 It finds coregulated clusters of miRNAs whose collective expression is strongly associated with the sample categories. Using the concept of rough hypercuboid equivalence partition matrix, the degree of dependency is calculated for miRNAs, which is used to compute both relevance and significance of the miRNAs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rough hypercuboid based supervised clustering of miRNAs

Paul

Vera

2015

Mol. BioSyst.

View full text Add to dashboard Cite

The microRNAs are small, endogenous non-coding RNAs found in plants, animals, and some viruses, which function in RNA silencing and post-transcriptional regulation of gene expression. It is suggested by various genome-wide studies that a substantial fraction of miRNA genes is likely to form clusters. The coherent expression of the miRNA clusters can then be used to classify samples according to the clinical outcome. In this regard, a new clustering algorithm, termed as rough hypercuboid based supervised attribute clustering (RH-SAC), is proposed to find such groups of miRNAs. The proposed algorithm is based on the theory of rough set, which directly incorporates the information of sample categories into the miRNA clustering process, generating a supervised clustering algorithm for miRNAs.The effectiveness of the new approach is demonstrated on several publicly available miRNA expression data sets using support vector machine. The so-called B.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. The association of the miRNA clusters to various biological pathways is also shown by doing pathway enrichment analysis.

show abstract

“…55 In the proposed method, a new rough hypercuboid based similarity measure is developed to calculate similarity between two miRNAs. Whereas, in ref.…”

Section: Supervised Mirna Clustering Algorithmmentioning

confidence: 99%

Section: Supervised Mirna Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rough hypercuboid based supervised clustering of miRNAs

Paul

Vera

2015

Mol. BioSyst.

View full text Add to dashboard Cite

show abstract

“…Venkatesh and Thangaraj (2008) have proposed a SOM based clustering and artificial intelligence technique to analyse patterns of soil distributed across a geographical area. Maji (2011) proposed a new clustering algorithm, termed as FuzzyRough Supervised Attribute Clustering (FRSAC), to find groups of coregulated genes whose collective expression is strongly associated with sample categories. A new quantitative measure is introduced based on fuzzy-rough sets that incorporates the information of sample categories to measure the similarity among genes whereby redundancy among the genes are removed.…”

Section: Related Workmentioning

confidence: 99%

A Rough Set based Gene Expression Clustering Algorithm

Emilyn¹,

Ramar²

2011

Journal of Computer Science

View full text Add to dashboard Cite

Problem statement: Microarray technology helps in monitoring the expression levels of thousands of genes across collections of related samples. Approach: 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. Results: Several clustering techniques have been proposed for identifying gene signatures and to understand their role and many of them have been applied to gene expression data, but with partial success. The main aim of this work was to develop a clustering algorithm that would successfully indentify gene patterns. The proposed novel clustering technique (RCGED) provides an efficient way of finding the hidden and unique gene expression patterns. It overcomes the restriction of one object being placed in only one cluster. Conclusion/Recommendations: The proposed algorithm is termed intelligent because it automatically determines the optimum number of clusters. The proposed algorithm was experimented with colon cancer dataset and the results were compared with Rough Fuzzy K Means algorithm.

show abstract

“…The advancement of web usage pose a new challenge for the researchers to develop effective document clustering algorithm to obtain effective results [4,5,10,11] with less computational task. Document clustering [6][7][8][9] is process of grouping web documents automatically based on occurrence of words as well as semantic information. Document clustering can be done in various ways like, partitional clustering and hierarchical clustering.…”

Section: Introductionmentioning

confidence: 99%

Document-Document similarity matrix and Multiple-Kernel Fuzzy C-Means Algorithm-based web document clustering for information retrieval

Yadav¹

2014

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

Due to continuous development of World Wide Web, web database are growing massively where automatic grouping of web documents pose a new challenge for researchers to easily retrieve the information. Literature presents different algorithms for web document clustering useful for information retrieval. In this work, Document-Document similarity matrix and Multiple-Kernel Fuzzy C-Means Algorithm-based web document clustering is developed for information retrieval. At first, web documents are read and initial pre-processing are applied to extract the important words. Then, feature space is constructed using keywords and its frequency. Subsequently, document to document similarity matrix is constructed using the similarity measure, called semantic retrieval measure (SR). The measure considers four different criteria, such as, the probability of occurrence in the document, probability of occurrence in the first document, probability of occurrence in the second document and probability of occurrence in both synonyms set. Based on this measure, D-D matrix is computed to do the final grouping using Multiple-Kernel Fuzzy C-Means Algorithm. The experimentation is done with 100 web documents and the results are evaluated with accuracy and entropy.

show abstract

Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data

Cited by 72 publications

References 21 publications

Rough hypercuboid based supervised clustering of miRNAs

Rough hypercuboid based supervised clustering of miRNAs

A Rough Set based Gene Expression Clustering Algorithm

Document-Document similarity matrix and Multiple-Kernel Fuzzy C-Means Algorithm-based web document clustering for information retrieval

Contact Info

Product

Resources

About