Algorithms for Frequent Itemset Mining and Database Sanitization

Name: Algorithms for Frequent Itemset Mining and Database Sanitization
Brand: VDM Verlag Dr. Müller
SKU: 9783639199086
Price: 24322 HUF
Availability: InStock

Data Mining

Szerző: Yu-Chiang Li

Nyelv:

Kötés: Puha kötésű

Kiadó: VDM Verlag Dr. Müller

Elérhetőség: Kiadói készleten rendelésre

Küldés 17-27 napon belül

24 322 Ft

Data mining techniques have been widely applied in numerous areas and represent an important field o...

Információk a könyvről

Szerző

Yu-Chiang Li

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2009

oldal

164

EAN

9783639199086

Enbook ID

06827949

Kiadó

VDM Verlag Dr. Müller

Súly

261

Méretek

150 x 220 x 10

Teljes leírás

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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Algorithms for Frequent Itemset Mining and Database Sanitization

Data Mining

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