Very few clustering methods are capable of clustering data without assuming the availability of operations which are defined only in strongly structured spaces, such as vector spaces. We propose an efficient data clustering method based on the shared near neighbours approach, which requires only a distance definition and is capable of discovering clusters of any shape. Using efficient data structures for querying metric data and a scheme for partitioning and sampling the data, the method can cluster effectively and efficiently data sets whose size exceeds the internal memory size.
Efficient shared near neighbours clustering of large metric data sets / Lodi S.; Reami L.; Sartori C.. - STAMPA. - 1704:(1999), pp. 424-429. (Intervento presentato al convegno 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 1999 tenutosi a cze nel 1999) [10.1007/978-3-540-48247-5_53].
Efficient shared near neighbours clustering of large metric data sets
Lodi S.;Sartori C.
1999
Abstract
Very few clustering methods are capable of clustering data without assuming the availability of operations which are defined only in strongly structured spaces, such as vector spaces. We propose an efficient data clustering method based on the shared near neighbours approach, which requires only a distance definition and is capable of discovering clusters of any shape. Using efficient data structures for querying metric data and a scheme for partitioning and sampling the data, the method can cluster effectively and efficiently data sets whose size exceeds the internal memory size.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
