Kniha Algorithms for Frequent Itemset Mining and Database Sanitization Yu-Chiang Li

Algorithms for Frequent Itemset Mining and Database Sanitization

Data Mining

Autor: Yu-Chiang Li
Jazyk: Angličtina
Väzba: Brožovaná
Dostupnosť: U vydavateľa na objednávku
Odosielame za 17-27 dní
66.83
Data mining techniques have been widely applied in numerous areas and represent an important field o...

Informácie o knihe

Autor
Jazyk
Angličtina
Väzba
Kniha - Brožovaná
Vydalo
2009
Stránok
164
EAN
9783639199086
Enbook ID
06827949
Hmotnosť
261
Rozmery
150 x 220 x 10

Kompletný popis

Data mining techniques have been widely applied in numerous areas and represent an important field of research. In Chapter 1, the research motivation, objectives and contributions are introduced. Chapter 2 introduces background work on data mining, share mining, utility mining, and privacy-preserving data mining. Chapter 3 describes the proposed NFP-growth method for discovering frequent itemsets. Chapters 4 through 6 explain several novel fast algorithms for share mining --- including FSM, EFSM, SuFSM, ShFSM, and DCG --- to efficiently generate all share- frequent itemsets. Furthermore, Chapter 7 presents the Isolated Items Discarding Strategy (IIDS), which can be applied to any existing level-wise share mining or utility mining method to reduce candidates and to improve its performance. Next, Chapter 8 introduces the proposed Maximum Item Conflict First (MICF) algorithm, which has a low sanitization rate and achieves a low misses cost, for hiding all restrictive itemsets. At the end of Chapters 3 through 8, the experimental results and evaluates the performance of the proposed algorithms are provided. Finally, Chapter 9 draws a summary of the dissertation.

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