As clustering algorithms become more and more sophisticated to cope with current needs, large data sets of increasing complexity, sampling is likely to provide an interesting alternative. The proposal is a distance-based algorithm: The idea is to iteratively include in the sample the furthest item from all the already selected ones. Density is managed within a postprocessing step, and either low- or high-density areas are considered. The algorithm has some nice properties: insensitive to initialization, data size and noise, it is accurate according to the Rand index and avoids many distance calculations thanks to internal optimization. Moreover, it is driven by only one, meaningful, parameter, called granularity, which impacts the sample size. Compared with concurrent approaches, it proved to be as powerful as the best known methods, with the lowest CPU cost.
['Frédéric Ros', 'Serge Guillaume']
DIDES: a fast and effective sampling for clustering algorithm