Unsupervised Learning with Clustering Techniques w/Srini Anand

As humans we are able to discern differences among different groups within a collection. We might group a collection by broad groups such as birds versus plants versus animals or detect subtle features to identify different makes and models of cars.

Clustering techniques allow us to automate the process and apply them to data where groupings are not immediately obvious. These techniques are used for different purposes such as detecting market segments, identifying properties of online communities, fraud detection, and cybersecurity.

Srini Anand is a Data Scientist at Ameritas Life Insurance Company and holds a Masters degree in Data Science from Indiana University.

In this talk, Srini will present different techniques that can be used, ways to measure completeness and accuracy, and to interpret the results. We’ll apply these techniques to simple examples and see how well they perform.

Refreshments will also be available.