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Discovering characteristic individual accessing behaviors in web environment

Identifieur interne : 000172 ( LNCS/Extraction ); précédent : 000171; suivant : 000173

Discovering characteristic individual accessing behaviors in web environment

Auteurs : LONG WANG [Allemagne] ; Christoph Meinel [Allemagne] ; CHUNNIAN LIU [République populaire de Chine]

Source :

RBID : Pascal:05-0447527

Descripteurs français

English descriptors

Abstract

Discovering diverse individual accessing behaviors in web environment is required before mining the valuable patterns from behaviors of groups of visitors. In this paper, we investigate the data preparation in web usage mining, and especially focus on discovering characteristic individual accessing behaviors and give a systematic and formalized study on this topic, Based on the target usage patterns, individual user behavior through the web site can be discovered into five different categories: granular accessing behavior, linear sequential behavior, tree structure behavior, acyclic routing behavior and cyclic routing behavior. We also give different algorithms for discovering different kinds of behaviors. The experimental studies show that our discovery of individual behavior is very useful and necessary in web usage mining.

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Pascal:05-0447527

Le document en format XML

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