(Re)designing Clustering Algorithms for Big Data

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Our current speed of data generation combined with storage capacity increases have given rise to new paradigms in computing. For example, according to the IBM's website, there are approximately 695,000 status updates and 11 million instant messages sent every minute on Facebook. However, many organizations have faced the problem of having a lot of data, but poor knowledge about them. Clustering methods help to automatically identify unobserved groups for a set of data objects, and are currently being radically transformed by the size, the variety and the nature of the available data, i.e., by so-called “Big Data”. This research program focuses on the development of scalable algorithms for Big data clustering. This will be achieved both by: (i) redesigning well-known successful serial algorithms for scalability, leveraging their main theoretical ideas; and (ii) developing new algorithms and heuristics using new programming paradigms associated with Big Data.