This survey paper, mainly focused on challenges of big data, how to extract the required data from large volume of data, and also various clustering algorithm. For the extraction of data, mapreduce function is used which is mainly used in Google search engine.doi:10.1007/978-981-16-3828-2_10X. A. PresskilaY. H. ...
The current era of Big Data [1], [2], [3] has forced both researchers and industries to rethink the computational solutions for getting useful insight on massive data produced in a wide variety of real life scenarios. In fact, a great deal of attention has been devoted to the design of...
In addition to the metaheuristic algorithm, the primary goal of the data mining process is to gather data from a big data set. The data can then be translated into a clear format for further usage. Clustering is a popular experimental data analysis tool. Objects are arranged using clustering ...
It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial ...
Although the tight clustering method is an intelligent algorithm that provides a reliable outcome in gene clustering, it fails to incorporate huge data of tens of thousands of gene expressions. These huge data sets, which are results of advances in biomedical imaging technologies and gene mapping te...
James G, Witten D, Hastie T, Tibshirani R (2015) An introduction to statistical learning with applications in R. Springer, New York MATH Google Scholar Jiang D, Wu S, Chen G, Ooi BC, Tan K-L, Xu J (2016) epiC: an extensible and scalable system for processing big data. VLDB J ...
It’s not a bad time to be a Data Scientist. Serious people may find interest in you if you turn the conversation towards “Big Data”, and the rest of the party crowd will be intrigued when you mention “Artificial Intelligence” and “Machine Learning”. EvenGoogle thinksyou’re not ba...
We develop scSTEM, single-cell STEM, a method for clustering dynamic profiles of genes in trajectories inferred from pseudotime ordering of single-cell RNA-seq (scRNA-seq) data. scSTEM uses one of several metrics to summarize the expression of genes and assigns a p-value to clusters enabling...
In order to recapitulate tumor progression pathways using epigenetic data, we developed novel clustering and pathway reconstruction algorithms, collectively referred to as heritable clustering. This approach generates a progression model of altered DNA m
in what concerns the analysis of gene expression data, as pointed by [1,31,32,34]. If on one hand diverse studies addressed the issue of clustering method selection, on the other hand just a few tried to provide guidelines regarding the selection of distances for gene expression data. Thus...