Use este identificador para citar ou linkar para este item: repositorio.ufla.br/jspui/handle/1/15026
Título: A Hash based Mining Algorithm for Maximal Frequent Item Sets using Linear Probing
Autor: Rahman, A. M.J. Md. Zubair
Balasubramanie, P.
Krihsna, P. Venkata
Palavras-chave: Mining-Frequent Item Sets-Hashing-Linear Probing-MAFIA etc
Publicador: Editora da UFLA
Data: 1-Mar-2009
Outras Identificações : http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/246
Descrição: Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. In this paper, we propose an algorithm, HBMFI-LP which hashing technology to store the database in vertical data format. To avoid hash collisions, linear probing technique is utilized. The proposed algorithm generates the exact set of maximal frequent itemsets directly by removing all nonmaximal itemsets. The proposed algorithm is compared with the recently developed MAFIA algorithm and is shown that the HBMFI-LP outperforms in the order of two to three
Idioma: eng
Aparece nas coleções:Infocomp

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