Web usage mining: discovery and applications of usage patterns from Web data

By Jaideep Srivastava, Robert Cooley, M. Deshpande, Pang-Ning Tan. SKDD, 2000

2,664 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5dc1597737c9912838d699299e1ff94c489bbc3d

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In Information Systems

Citations in the corpus

Frequent pattern mining: current status and future directions (2007, methodology): “Web access patterns via association rule mining in web logs were proposed by (Chen et al. 1996; Pei et al. 2000; Srivastava et al. 2000; Punin and Krishnamoorthy 2001).”
Building a Recommender Agent for e-Learning Systems (2002, positive): “[ 15 ]. Some new experimental tools use data mining techniques to extract hidden patterns from the large web logs.”
Enterprise Data Analysis and Visualization: An Interview Study (2012, background): “Some analysts (16/35) noted difficulty performing ad hoc grouping of observations, as in path or funnel analysis [36].”
Applying Data Mining Techniques to e-Learning Problems (2007, methodology): “A framework for the use of Web usage mining to support the validation of learning site designs was defined in [21], applying association and sequence techniques [80].”
A taxonomy of sequential pattern mining algorithms (2010, methodology): “Previous surveys looked at different mining methods applicable to web logs [Srivastava et al. 2000; Facca and Lanzi 2003; Masseglia et al. 2005; Facca and Lanzi 2005; Iváncsy and Vajk 2006], but they lack three important things: they fail (i) to focus on sequential patterns as a complete solution; (”
Recommender Systems (2016, neutral): “Sequential pattern-mining methods for predicting accesses in Web logs are discussed in [208, 440, 442, 443, 562].”
Effective personalization based on association rule discovery from web usage data (2001, background): “In recent years there has been an increasing interest and a growing body of work in Web usage mining [16] as an underlying approach to capturing and modeling Web user behavioral patterns and for deriving e-business intelligence.”
Web mining for web personalization (2003, neutral): “WebSIFT [Cooley et al. 1999b,a; Srivastava et al. 2000] Server .”
Monitoring web browsing behavior with differential privacy (2014, background): “• detecting interesting, new contents on the web [13, 24] ,”
Sequential pattern mining -- approaches and algorithms (2013, methodology): “In the majority of cases the data has been stored as transactional datasets and similar techniques such as those used by association rule miners [Ceglar and Roddick 2006] have been employed in the discovery process.”
Educational data mining: A survey from 1995 to 2005 (2007, methodology): “There are several types of logs (Srivastava et al., 2000): • Server log file.”