Frequent Pattern Mining in Transactional and Structured Databases | Different aspects of itemset, sequence and subtree discovery. Cet article n’est pas disponible.
Langue : anglais
Edité par LAP LAMBERT Academic Publishing, 2010
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- Neuf
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A propos de cet article
Frequent Pattern Mining in Transactional and Structured Databases | Different aspects of itemset, sequence and subtree discovery | Renáta Iváncsy | Taschenbuch | 144 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843359740 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
N° de réf. du vendeur 107273346
- Titre
- Frequent Pattern Mining in Transactional and Structured Databases | Different aspects of itemset, sequence and subtree discovery
- Auteur
- Renáta Iváncsy
- Éditeur
- LAP LAMBERT Academic Publishing
- Année de publication
- 2010
- État de l'article
- Neu
- Reliure
- Taschenbuch
- Langue
- anglais
- ISBN à 10 chiffres
- 3843359741
- ISBN à 13 chiffres
- 9783843359740
- Poids de l'article
- 233 grammes
- Dimensions
- 220 x 150 x 10 mm
- Catalogues du vendeur
- Bücher
Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements.
« Synopsis » peut appartenir à une autre édition de cet ouvrage.
Présentation de l'éditeur
Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements.
« A propos de ce titre » peut appartenir à une autre édition de cet ouvrage.