Fast Streaming Behavioural Pattern Mining

Chovanak, Tomas; Kassak, Ondrej; Kompan, Michal; Bieliková, Mária

doi:10.1007/s00354-018-0044-4

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The user behaviour can be described by multiple patterns and statistical distributions [29]. Despite this characteristic, it is still a complex set of actions where every decision is a result of the subconscious consideration of multiple factors (e.g., actual information need, preferences or previous knowledge).…”

Section: Discussionmentioning

confidence: 99%

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Kassak

Kompan

Bieliková

2018

JWE

Self Cite

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User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on the Web. The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reflect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The longterm behaviour describes relatively stable user preferences based on extensive user history. As the Web has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-ofthe-art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper, we provide a comprehensive survey

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Section: Discussionmentioning

confidence: 99%

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Kassak

Kompan

Bieliková

2018

JWE

Self Cite

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“…But, this approach requires more time for mining the user behaviour. Hybpmine was presented in [10] to increases the performance of mining the behavioural patterns over a data stream. However, accuracy of behavioural pattern mining was lower.…”

Section: Introductionmentioning

confidence: 99%

Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining

G.S.Raman¹,

Christ²,

Raj³

2021

IJIES

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Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user’s behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIPBSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIPBSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods.

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Managing the Data Meaning in the Data Stream Processing: A Systematic Literature Mapping

Diván

Sánchez-Reynoso

2020

Algorithms for Intelligent Systems

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Fast Streaming Behavioural Pattern Mining

Cited by 5 publications

References 23 publications

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Acquisition and Modelling of Short-TermUser Behaviour on theWeb: A Survey

Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining

Managing the Data Meaning in the Data Stream Processing: A Systematic Literature Mapping

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