AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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STUDIA INFORMATICA - Ediţia nr.1 din 2005 | |||||||
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CORE BASED INCREMENTAL CLUSTERING. Autori: GABRIELA ŞERBAN, ALINA CÂMPAN. |
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Rezumat: Clustering is a data mining activity that aims to differentiate groups inside a given set of objects, with respect to a set of relevant at- tributes of the analyzed objects. Generally, existing clustering methods, such as k-means algorithm, start with a known set of objects, measured against a known set of attributes. But there are numerous applications where the attribute set characterizing the objects evolves. We propose in this paper an incremental, k-means based clustering method, Core Based Incremental Clustering (CBIC), that is capable to re-partition the objects set,when the attributes set increases. The method starts from the partitioning into clusters that was established by applying k-means or CBIC before the attribute set changed. The result is reached more eficiently than running k-means again from the scratch on the feature-extended object set. Experiments proving the method''s eciency are also reported. | |||||||