DIDES: a fast and effective sampling for clustering algorithm

DIDES: un algorithme d'échantillonnage pour le clustering rapide et efficace

F. Ros (1) , S. Guillaume (2)

1 UO - Université d'Orléans
2 UMR ITAP - Information – Technologies – Analyse Environnementale – Procédés Agricoles

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 post-processing step, 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.

Mots clés: ALGORITHM; DENSITY; SAMPLING

Mots clés: ECHANTILLONNAGE; MATHEMATIQUES; ALGORITHME; DENSITE

Domaines: Sciences de l'environnement

F. Ros, S. Guillaume. DIDES: a fast and effective sampling for clustering algorithm. Knowledge and Information Systems (KAIS), 2017, 50 (2), pp.543-568. ⟨10.1007/s10115-016-0946-8⟩. ⟨hal-01707857⟩