This paper presents a partitioned method which crosses the limitations of traditional approaches to clustering of transactional data. A modification of the stanard K-Means algorithm is presented, which has a good scalability on the number of objects and attributes, but can only work with numeric vectors of fixed length.
Clustering transactional data
Giannotti F;
2002
Abstract
This paper presents a partitioned method which crosses the limitations of traditional approaches to clustering of transactional data. A modification of the stanard K-Means algorithm is presented, which has a good scalability on the number of objects and attributes, but can only work with numeric vectors of fixed length.File in questo prodotto:
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Descrizione: Clustering transactional data
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