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Aster Lens is a new interactive Web application for Aster 5.10.  It allows Users to find, view, and share results from their nPathViz and cFilterViz functions.  It’s a quantum leap forward compared to the old way of visualizing where the answer set would provide you with a URL that you have to Copy/Paste into a Web browser.  This presentation will cover the basics on how to setup, configure and use Aster Lens.

 

Installation

When learning Aster Data, the first thing we did was logon to Aster Command Terminal (ACT) and start submitting queries.  This is typically done through a SSH client (like Putty).  To customize the ACT interface, I used the Putty options to change the background and foreground colors and change the font and font size.  Later on in the training, we were introduced to Eclipse, which provided additional functionalities that I found compelling.

I’m assuming you’re already read Aster nPath functionality (Volume 1) and and are now ready to go into a deep dive with Use Case scenarios.  First we’ll cover some additional concepts, and then move into real-life examples that bring out the value of nPath.  Here’s the lesson plan:

Regression is a method of doing analysis.  Basically it helps you predict the future.  Businesses use these models to help explain customer behavior which can make them more profitable. 

But wait a minute, who started this whole Regression thing?  The guy who invented Regression, Sir Francis Galton, was studying how the height of fathers predicted the height of their sons.  He showed on average that short fathers had taller sons and tall fathers had shorter sons.  He called this condition ‘regression to mediocrity’ and the term stuck.

Retailers mine transaction data to track purchasing behavior.  Some of the more popular are Market Basket and Collaborative Filtering

By understanding what products customers tend to purchase, a vendor can maximize their sales for that customer.  Armed with this information, an analyst can initiate:

This presentation will cover some of the finer points on Aster’s nPath function.  Here’s the overview:

Since taking the Aster Data class, I wanted to learn more about this exciting new technology.  After downloading the 2 VMware images (Queen and Worker), I needed another Worker node to be able to increase the Replication Factor (RF) from 1 to 2.

Naïve Bayes is a set of functions to train a classification model.  A training data set for which we know the outcome (Predictor column) based on input variable columns are used to generate the model. 

We then run the model against a set of input variables for which we do not know the Predictor to see what the model says.  It’s quite similar to a Decision Tree with one big exception; the input data are independent of one other.  This is a strong assumption but it makes the computation of the model extremely simple.

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