Extending the K-Nearest Neighbour Classification Algorithm to Symbolic Objects
نویسندگان
چکیده
Riassunto: L’analisi di dati simbolici generalizza alcuni metodi statistici standard al caso di oggetti simbolici (SO). Questi oggetti, informalmente definiti “dati aggregati”, poiché sintetizzano le informazioni relative ad un gruppo di individui, possono essere confrontati al fine di individuare dei cluster, di classificarli o ordinarli in base al loro grado di generalizzazione. L’articolo propone un’estensione dell’algoritmo classico di classificazione K-Nearest Neighbor a tali oggetti. Il risultato di questo algoritmo è ancora un insieme di oggetti simbolici che possono essere studiati mediante altre tecniche di analisi di dati simbolici.
منابع مشابه
Classification of symbolic objects: A lazy learning approach
Symbolic data analysis aims at generalizing some standard statistical data mining methods, such as those developed for classification tasks, to the case of symbolic objects (SOs). These objects synthesize information concerning a group of individuals of a population, eventually stored in a relational database, and ensure confidentiality of original data. Classifying SOs is an important task in ...
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