Clustering is a widely used unsupervised data mining technique. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction set by the density. In this paper we present a software system called DBStrata that implements the density-based clustering architecture together with several extensions able to boost the clustering performances and to efficiently identify outliers.
|Titolo:||DBStrata: a system for density-based clustering and outlier detection based on stratification|
|Data di pubblicazione:||2011|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|