Clustering of Web Sessions Using Levenshtein Metric

Scherbina, Andrei; Кузнецов, С. Д.

doi:10.1007/978-3-540-30185-1_14

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…• Levenshtein: the minimum number of edition operations (insertions, deletions, substitutions) to transform one sequence of symbols into the other [9], [11].…”

Section: Metricmentioning

confidence: 99%

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Learning from User Workflows for the Characterization and Prediction of Software Crashes

Chloe¹,

Antoine²,

Cournède³

2016

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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Reducing as much as possible the rate of software crashes is crucial especially in medical applications. In this paper, we make the assumption that crashes result from the user workflow, that is to say the sequence of user actions. Our objective is thus to identify root causes of crashes and to anticipate them in real-time, based on the analysis of the sequences of user actions. For these purposes, we introduce two methods. The first one consists in using graph-based representations to detect combinations of user actions having a high probability to provoke a software crash, thus identifying crash signatures and helping for problem resolution. The second one, based on clustering of user sessions, is a real-time monitoring method, computing a crash probability at each new user action. Test cases show promising results for both methods. Our representation of user session as 'Graph-of-Actions' enabled the identification of some significant crash signatures while revealing the impact of successive actions dependence on crash causes. Likewise, our clustering based method for session monitoring resulted in promising values of sensitivity and specificity for some specific clustering configurations.

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“…• Levenshtein: the minimum number of edition operations (insertions, deletions, substitutions) to transform one sequence of symbols into the other [9], [11].…”

Section: Metricmentioning

confidence: 99%

“…Although not dedicated to anomaly detection, [9] is interesting since it uses a customized Levenshtein metric to cluster web sessions of variable lengths, this metric will also be tested for the application of our proposed method.…”

Section: Introductionmentioning

confidence: 99%

Learning from User Workflows for the Characterization and Prediction of Software Crashes

Chloe¹,

Antoine²,

Cournède³

2016

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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show abstract

“…Some other models, proposed in [9,29] have intended to capture difference by user decisions and the cost that represent in navigating changing sequences of pages.…”

Section: Similarity and Dissimilarity Measures In Web Usage Miningmentioning

confidence: 99%

A new dissimilarity measure for online social networks moderation

Ríos

Silva

2013

Web Intelligence and Agent Systems: An International Journal

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Online Social Networks (OSN) and Virtual Communities (VC) software are vital tools useful to connect organizations with customers or community members. As these tools become more ubiquitous with general population, different managment problems start to arise. As these members' interactions become large, it is impossible for manual handling of moderation tasks without automatic or semi-automatic techniques.Of course, web mining techniques are very useful for understanding text patterns or browsing patterns in Websites, opening an opportunity to develop new algorithms to discover community members' patterns which have to be moderated. There have been previous work done on text patterns discovery for moderation that have reduced the moderation task to finding spammers. However, the moderation problem is much more complex: it involves not only text but also behavior patterns (from bulling of other members to fights between them). We present a dissimilarity measure which includes human interaction aspects combined with these interactions' contents semantics (not just free words of text). We show how we successfully applied our method into a real Virtual Community of Practice to detect users that should be moderated.

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“…• Similarity-Based Approach: In order to decide whether two sessions are clustered together, a distance function (similarity measure) must be defined in advance. Distance functions (e.g., Euclidean, Manhattan, Levenshtein [Scherbina & Kuznetsov, 2004], etc.) can be determined either directly or indirectly, although the latter is more common in applications.…”

Section: Identifying Web User Clustersmentioning

confidence: 99%

“…Hierarchical Clustering Algorithm (Scherbina & Kuznetsov, 2004) Web users' sessions Similarity based EM (Cadez et al, 2003) Web users' sessions Model based…”

Section: Research Work Cluster Content Clusteringmentioning

confidence: 99%

Clustering Web Information Sources

Vakali

Pallis

Angelis

Personalized Information Retrieval and Access

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The explosive growth of the Web scale has drastically increased information circulation and dissemination rates. As the number of both Web users and Web sources grows significantly everyday, crucial data management issues, such as clustering on the Web, should be addressed and analyzed. Clustering has been proposed towards improving both the information availability and the Web users’ personalization. Clusters on the Web are either users’ sessions or Web information sources, which are managed in a variation of applications and implementations testbeds. This chapter focuses on the topic of clustering information over the Web, in an effort to overview and survey on the theoretical background and the adopted practices of most popular emerging and challenging clustering research efforts. An up-to-date survey of the existing clustering schemes is given, to be of use for both researchers and practitioners interested in the area of Web data mining.

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Clustering of Web Sessions Using Levenshtein Metric

Cited by 7 publications

References 10 publications

Learning from User Workflows for the Characterization and Prediction of Software Crashes

Learning from User Workflows for the Characterization and Prediction of Software Crashes

A new dissimilarity measure for online social networks moderation

Clustering Web Information Sources

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