Efficient Techniques for Patterns Analysis using Web Usage Mining

Brijesh Bakariya

ISBN 10: 6139858712 / ISBN 13: 9786139858712

Verlag: LAP LAMBERT Academic Publishing, 2018

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - The book is composed of seven chapters. Chapter 1 gives an introduction to web usage mining. In this research work, some approaches are developed to predict the users' behaviour from weblogIn this chapter, the background and motivation for this research work are explained. Chapter 1 describes the organization of the book. The related works in this area are discussed in Chapter 2. An existing literature survey includes the user behaviour prediction, frequent pattern mining, high utility and top-k HUIM itemset mining from weblog. Chapter 3 describes preprocessing techniques applied for weblog and also describes a pattern mining approach for SNS. Chapter 4 describes a high utility itemsets from weblog. High utility itemset refers to those set of items which has high utility such as profit in a database. Chapter 5 describes rare itemset mining from weblog. Chapter 6 explains the top-k high utility itemsets mining named TKHUIM for mining most high utility itemsets and it is based on frequent pattern mining. Chapter 7 concludes the book and also discussed the future enhancements. In this chapter, the outcome of this research work is explained.

Bestandsnummer des Verkäufers 9786139858712

Bibliografische Details

• Titel: Efficient Techniques for Patterns Analysis using Web Usage Mining
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 6139858712
• ISBN-13: 9786139858712
• Einband: Taschenbuch
• Zustand: Neu

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Inhaltsangabe

The book is composed of seven chapters. Chapter 1 gives an introduction to web usage mining. In this research work, some approaches are developed to predict the users' behaviour from weblogIn this chapter, the background and motivation for this research work are explained. Chapter 1 describes the organization of the book. The related works in this area are discussed in Chapter 2. An existing literature survey includes the user behaviour prediction, frequent pattern mining, high utility and top-k HUIM itemset mining from weblog. Chapter 3 describes preprocessing techniques applied for weblog and also describes a pattern mining approach for SNS. Chapter 4 describes a high utility itemsets from weblog. High utility itemset refers to those set of items which has high utility such as profit in a database. Chapter 5 describes rare itemset mining from weblog. Chapter 6 explains the top-k high utility itemsets mining named TKHUIM for mining most high utility itemsets and it is based on frequent pattern mining. Chapter 7 concludes the book and also discussed the future enhancements. In this chapter, the outcome of this research work is explained.