I've recently come across the 'googleVis' R package. This allows you to create a variety of different (typical and standard) charts in R but with the look and feel of the charts we can get from a number of different Google sites.
I won't bore you with some examples in the post but I'll point you to a good tutorial on the various charts.
Here is the link to the mini-tutorial.
Before you can use (more...)
This is the third blog post on my series on examining the Clusters that were predicted by an Oracle Data Mining model. Check out the previous blog posts.
In the previous posts we were able to list the predicted cluster for each record in our data set. This is the cluster that the records belonged (more...)
This is the second blog post of my series on examining the clusters that are predicted for by an Oracle Data Mining model for your data. In my previous blog post I should you how to use CLUSTER_ID and CLUSTER_PROBABILITY functions. These are the core of what you will be used when working with clusters and automating the process.
In this blog post I will look at what details are used by the clustering (more...)
In a previous blog post
I gave some details of how you can examine some of the details behind a prediction made using a classification model. This seemed to spark a lot of interest. But before I come back to looking at classification prediction details and other information, this blog post is the first in a 4 part blog post on examining the details of Clusters, as identified by a cluster model created using Oracle (more...)
When building predictive models the data scientist can spend a large amount of time examining the models produced and how they work and perform on their hold out sample data sets. They do this to understand is the model gives a good general representation of the data and can identify/predict many different scenarios. When the "best" model has been selected then this is typically deployed is some sort of reporting environment, where a list is (more...)
Oracle Data Visualisation Desktop has the feature of being able to include some advanced analytics. In a previous blog post I showed you how to go about installing Oracle R Distribution on your desktop/client machine. This will allow you to make use of some of the advanced analytics features of Oracle Data Visualization Desktop.
The best way to get started with using the advanced analytics features of Oracle Data Visualization Desktop, is to ignore that (more...)
Oracle Data Visualization comes with all the typical features you have with Visual Analyzer that is part of BICS, DVCS and OBIEE.
An additional install you may want to do is to install the R language for Oracle Data Visualization Desktop. This is required to enable the Advanced Analytics feature of the tool.
After installing Data Visualisation Desktop when you open the Advanced Analytics section and try to add one of the Advanced Analytics graphing (more...)
Using Oracle Data Visualisation is just the same or very similar as to using the Cloud version of the tool.
In this blog post I will walk you through the steps you need to perform the first time you use the Oracle Data Visualization client tool and to quickly create some visualizations.
Step 1 - Create a Connection to your Oracle DB and Schema
After opening Oracle Data Visualisation client tool client on the Data (more...)
After a bit of a long wait Oracle have finally release Oracle Data Visualization for the desktop. The desktop version of this tool is only available for Windows desktops at the moment. I'm sure Oracle will be bringing out versions of other OS soon (I hope).
To get you hands on the Oracle Data Visualization to to the following OTN webpage (click on this image)
After downloading has finished, you can run the installer.
When you install R you also get a set of pre-compiled datasets. These are great for trying out many of the features that are available with R and all the new packages that are being produced on an almost daily basis.
The exact list of data sets available will depend on the version of R that you are using.
To get the list of available data sets in R you can run the following.