Data Clustering? don’t worry about the algorithm.

Data Clustering? don’t worry about the algorithm.

Introduction post of Data Clustering Tuning published on AgilOne blog on May 2013.

We are constantly pushing to improve our underlying algorithms and make them as adaptive as possible. Taking a step back, our problem generally is to fit classes of models and algorithms to customer data sets of varying data quality. In addition, we need to automate this so that we can scale delivery of our offerings from a business perspective.

This high-level business goal boils down to a number of technical requirements. It means we need to find ways of automatically evaluating results based on customer data and adaptations, and we need to do this in many different contexts.

One of our engineers, Gianmario Spacagana[1] took a fresh look at how to tune clustering algorithms. In this blog post, I will briefly introduce validation of clustering algorithms so that you can later more easily appreciate and understand Gianmario’s upcoming blog post.

Published by

Gianmario

Data Scientist with proven experience of building machine learning products across different industries. Currently leading the AI team at Helixa. Co-author of the book "Python Deep Learning", contributor to the “Professional Manifesto for Data Science” and founder of the DataScienceMilan.org community. My favorite hobbies include home cooking, martial arts, and exploring the surrounding nature while traveling by motorcycle.

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