Könyv Foundations of Data Mining and Knowledge Discovery Tsau Young Lin

Foundations of Data Mining and Knowledge Discovery

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: Beszállítói készleten alacsony példányszámban
Küldés 13-18 napon belül
61 807 Ft
"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2005
oldal
378
EAN
9783540262572
ISBN
3540262571
Enbook ID
01560767
Súly
1620
Méretek
156 x 234 x 28

Teljes leírás

"Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications. TOC:From the contents: §Part I Foundations of Data Mining; Knowledge Discovery as Translation; Mathematical Foundation of Association Rules Mining Associations by Solving Integral Linear Inequalities; Comparative Study of Sequential Pattern Mining Models; Designing Robust Regression Models; A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases; A Careful Look at the Use of Statistical Methodology in Data Mining; Justification and Hypothesis Selection in Data Mining.- Part II Methods of Data Mining; A Comparative Investigation on Model Selection in Binary Factor Analysis; Extraction of Generalized Rules with Automated Attribute Abstraction; Decision Making Based on Hybrid of Multi-knowledge and Na

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