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.

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About Gianmario

Data Scientist with experience on building data-driven solutions and analytics for real business problems. His main focus is on scaling machine learning algorithms over distributed systems. Co-author of the Agile Manifesto for Data Science (datasciencemanifesto.com), he loves evangelising his passion for best practices and effective methodologies amongst the data geeks community.
Link | This entry was posted in Amazon EC2, Cloud, Clustering, Java, Machine Learning, Open Source, Software Development and tagged , , , . Bookmark the permalink.

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