Mining Access Patterns Efficiently from Web Logs

Pei, Jian; Han, Jiawei; Mortazavi-Asl, Behzad; Zhu, Haojin

doi:10.1007/3-540-45571-x_47

Cited by 365 publications

(268 citation statements)

References 2 publications

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“…Web access patterns have been defined by Pei et al based on the problem statement of sequential patterns mining [4]. In general, a web log can be regarded as a sequence of user identifier and event pairs.…”

Section: Web Access Patternsmentioning

confidence: 99%

“…This technique has been applied in the web usage context to discover web access patterns (WAP) and to capture frequent navigation paths among user trials. WAP-tree was introduced to facilitate development of algorithms for mining access patterns from pieces of web logs [4]. Analysis of these access patterns allows Internet-based organisations to understand user preferences and predict future visit patterns.…”

Section: Introductionmentioning

confidence: 99%

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Applications of Concurrent Access Patterns in Web Usage Mining

Lü

Keech

Wang

2013

Data Warehousing and Knowledge Discovery

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Abstract. This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access patterns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with realworld datasets highlight the applicability of the approach in web navigation.

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Section: Web Access Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of Concurrent Access Patterns in Web Usage Mining

Lü

Keech

Wang

2013

Data Warehousing and Knowledge Discovery

View full text Add to dashboard Cite

show abstract

“…There are lot of approaches dealing with web usage mining for the purpose of finding the interesting information (or) automatically discover the user pattern to improve the purpose of web site design. Pei et al [12] have successfully used the log data from Web logs to discover frequent patterns, they proposed an algorithm called (WAP) Web access pattern tree for efficient mining of access patterns from pieces of logs, Murate et al [13] highlights the importance of analyzing users web log data and extracting their interests of web-watching behaviors and describes a method for clarifying users interests based on the analysis of the site-keyword graph, while Borges et al [14] modeled users' to capture Web navigation patterns. Dr.K.Iyakutti and P.Arun [11] also proposed a web personalized system in order to understand the behavior of the users and also to improve web site design.…”

Section: Related Workmentioning

confidence: 99%

Generating User Interesting Page (UIP) Using Supported Weight Value

Kyaw¹

2012

IJCATR

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Abstract:With an increasing continuous growth of information in WWW, it is very difficult to extract useful and relevant information from the huge amount of information. So, without any help on the system, the user may spend more time to get the interested information from the website. To solve above the problem, we proposed an approach for generating user interesting page (UIP) using weight value on web log data by associating with web usage mining techniques. Web usage mining, a classification of Web minin g, is the application of data mining techniques to discover usage patterns from click stream data. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. The goal of our system is to analyze user behaviours by mining enriched web access log data and create a top web page for the user with common needs or interests. This paper also focuses on to provide an overview how to generate frequent access pattern for the users from a Web log database without the use of domain specific ontology.

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“…Optimization techniques include (1) bi-level projecting for reducing the number and sizes of projected databases, and (2) Pseudo-projection for projecting memory-only databases, where each projection consists of the pointer to the sequence and offset of the postfix to the sequence. Pei et al (2000) proposed an algorithm using WAP-tree, which stands for web access pattern tree. This approach is quite different from the Apriori-like algorithms.…”

Section: Related Workmentioning

confidence: 99%

Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree

Ezeife

2005

Data Min Knowl Disc

102

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Abstract. Sequential mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of events. An important application of sequential mining techniques is web usage mining, for mining web log accesses, where the sequences of web page accesses made by different web users over a period of time, through a server, are recorded. Web access pattern tree (WAP-tree) mining is a sequential pattern mining technique for web log access sequences, which first stores the original web access sequence database on a prefix tree, similar to the frequent pattern tree (FP-tree) for storing non-sequential data. WAP-tree algorithm then, mines the frequent sequences from the WAP-tree by recursively re-constructing intermediate trees, starting with suffix sequences and ending with prefix sequences. This paper proposes a more efficient approach for using the WAP-tree to mine frequent sequences, which totally eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. The proposed algorithm builds the frequent header node links of the original WAP-tree in a pre-order fashion and uses the position code of each node to identify the ancestor/descendant relationships between nodes of the tree. It then, finds each frequent sequential pattern, through progressive prefix sequence search, starting with its first prefix subsequence event. Experiments show huge performance gain over the WAP-tree technique.

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Mining Access Patterns Efficiently from Web Logs

Cited by 365 publications

References 2 publications

Applications of Concurrent Access Patterns in Web Usage Mining

Applications of Concurrent Access Patterns in Web Usage Mining

Generating User Interesting Page (UIP) Using Supported Weight Value

Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree

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