Sessions at Open Source Bridge 2009 about CouchDB and cluster analysis

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Wednesday 17th June 2009

  • Clustering Data -- How to Have Fun in n-Dimensions

    by Jesse Hallett

    I will introduce the theory and goals of clustering algorithms. The literature in statistical analysis is made up of dense mathematical equations; so I will translate equations into pseudocode to make the topic more accessible to programmers.

    I will expand on the theoretical discussing by demonstrating a simple example of a clustering problem: how to group volcanos in Alaska by geographical proximity. I will move on to algorithms with real-world applications, such as how to group users with similar tastes given a database of user ratings.

    I may touch on more advanced techniques to improve the accuracy of resulting clusters. I will also discuss current limitations of statistical analysis. As an example, Netflix' ongoing competition for an algorithm that can predict whether or not a user will like the movie Napolean Dynamite.

    The examples from the talk will be implemented using JavaScript and CouchDB. My hope is that people from many different language and environment backgrounds will have some experience with JavaScript. And the data-processing capabilities of CouchDB are well suited to clustering algorithms.

    At 3:50pm to 4:35pm, Wednesday 17th June