571 search results for "3 d clusters"

Easy 3-Minute Guide to Making apply() Parallel over Distributed Grids and Clusters in R

September 1, 2013
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Easy 3-Minute Guide to Making apply() Parallel over Distributed Grids and Clusters in R

Last week I attended a workshop on how to run highly parallel distributed jobs on the Open Science Grid (osg). There I met Derek Weitzel who has made an excellent contribution to advancing R as a high performance computing language by developing BoscoR. BoscoR greatly facilitates the use of the already existing package “GridR” by The post Easy...

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Tracking Number of Historical Clusters in DOW 30 and S&P 500

February 4, 2013
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Tracking Number of Historical Clusters in DOW 30 and S&P 500

In the Tracking Number of Historical Clusters post, I looked at how 3 different methods were able to identify clusters across the 10 major asset universe. Today, I want to share the impact of clustering on the larger universe. Below I examined the historical time series of number of clusters in the DOW 30 and

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[Bioc 3.5] NEWS of my BioC packages

May 19, 2017
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I have 8 packages published within the Bioconductor project. ChIPseeker clusterProfiler DOSE ggtree GOSemSim meshes ReactomePA treeio A new package treeio was included in BioC 3.5 release. ChIPseeker Bug fixed of intron rank and optimized getGeneAnno function. clusterProfiler Defined simplify generics as it was removed from IRanges. enrichGO now supports ont="ALL" and will...

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Massively-parallel computations on Azure clusters with R, made easy with doAzureParallel

March 29, 2017
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Massively-parallel computations on Azure clusters with R, made easy with doAzureParallel

by JS Tan (Program Manager, Microsoft) For users of the R language, scaling up their work to take advantage of cloud-based computing has generally been a complex undertaking. We are therefore excited to announce doAzureParallel, a lightweight R package built on Azure Batch that allows you to easily use Azure’s flexible compute resources right from your R session. The...

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Finding Optimal Number of Clusters

February 9, 2017
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Finding Optimal Number of Clusters

In this post we are going to have a look at one of the problems while applying clustering algorithms such as k-means and expectation maximization that is of determining the optimal number of clusters. The problem of determining what will be the best value for the number of clusters is often not very clear from Related Post

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Gene homology Part 3 – Visualizing Gene Ontology of Conserved Genes

January 4, 2017
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Gene homology Part 3 – Visualizing Gene Ontology of Conserved Genes

Which genes have homologs in many species? In Part 1 and Part 2 I have already explored gene homology between humans and other species. But there I have only considered how many genes where shared between the species. In this post I want to have a cl...

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Network Analysis Part 3 Exercises

October 20, 2016
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Network Analysis Part 3 Exercises

This is the third set of exercises on networks in which we practice the functions for graph structure, using package igraph. The first and second part are available here: Part 1 Part 2 If you don’t have package already installed, install it using the following code: install.packages("igraph") and load it into the session using the

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[Bioc 34] NEWS of my BioC packages

October 19, 2016
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I have 7 packages published within the Bioconductor project. ChIPseeker clusterProfiler DOSE ggtree GOSemSim meshes ReactomePA A new package meshes was included in BioC 3.4 release. ChIPseeker For ChIPseeker, this release fixed several minor bugs. GEO data was updated, now ChIPseeker contains 20947 bed file information. CHANGES IN VERSION 1.9.8 ------------------------ o plotAvgProf/plotAvgProf2 order of panel by names of input tagMatrix List <2016-09-25, Sun> ...

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Network visualization – part 6: D3 and R (networkD3)

October 4, 2016
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Network visualization – part 6: D3 and R (networkD3)

I was never that much into JavaScript until I was introduced to D3.js. This open source JS library provides the features for dynamic data manipulation and visualization and allows users to become active participants in data visualization process. As such, … Continue reading →

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Customer Segmentation Part 3: Network Visualization

September 30, 2016
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Customer Segmentation Part 3: Network Visualization

This post is the third and final part in the customer segmentation analysis. The first post focused on K-Means Clustering to segment customers into distinct groups based on purchasing habits. The second post takes a different approach, using Pricipal C...

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