Web sessions clustering using hybrid sequence alignment measure (HSAM)

Poornalatha, G; Prakash, Shiv

doi:10.1007/s13278-012-0070-z

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…In this category, approaches that take both quantitative and qualitative aspects when considering the similarity of web sessions are presented in Table 6. HSAM (Poornalatha and Prakash, 2013) incorporates SAM into SABDM to take advantage of their effectiveness and reduce shortcomings of the two approaches. In this manner, distance between sequences is measured through uncommon elements and number of aligned element with or without gap insertions.…”

Section: Considering Sequences With Different Lengths Their Element Order Local and Global Matchingmentioning

confidence: 99%

A review of alignment based similarity measures for web usage mining

et al. 2019

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In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their common interest. An adequate web session clustering implementation significantly depends on the measure that is used to evaluate the similarity of sessions. An efficient approach to evaluate session similarity is sequence alignment, which is known as the task of determining the similarity of elements between sequences. In this paper, we review and compare sequence alignment-based measures for web sessions, and also discuss sequence similarity measures that are not alignment-based. This review also provides a perspective of sequence similarity measures that manipulate web sessions in usage clustering process.

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Section: Considering Sequences With Different Lengths Their Element Order Local and Global Matchingmentioning

confidence: 99%

A review of alignment based similarity measures for web usage mining

et al. 2019

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“…Other important descriptions of behaviors are click-stream [20] and web session [21] that reflect users' behavior patterns. However, it is still a blank towards the students' operation series.…”

Section: Related Workmentioning

confidence: 99%

A behavioral sequence analyzing framework for grouping students in an e-learning system

Xie

Zheng

Zhang

2016

Knowledge-Based Systems

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Grouping of students benefits the formation of virtual learning communities, and contributes to collaborative learning space and recommendation. However, the existed grouping criteria are mainly limited in the learning portfolios, profiles, and social attributes etc. In this paper, we aim to build a unified framework for grouping students based on the behavioral sequences and further predicting which group a newcomer will be. The sequences are represented as a series of behavioral trajectories. We discuss a shape descriptor to approximately express the geometrical information of trajectories, and then capture the structural, micro, and hybrid similarities. A weighted undirected graph, using the sequence as a node, the relation as an edge, and the similarity as the weight, is constructed, on which we perform an extended spectral clustering algorithm to find fair groups. In the phase of prediction, an indexing and retrieval scheme is proposed to assign a newcomer to the corresponding group. We conduct some preliminary experiments on a real dataset to test the availability of the framework and to determine the parameterized conditions for an optimal grouping. Additionally, we also experiment on the grouping prediction with a synthetic data generator. Our proposed method outperforms the counterparts and makes grouping more meaningful.

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Grouping Users Through Pair Wise Sequence Alignment and Graph Traversal Based on Web Page Navigation Behaviour

GeethaRamani

Revathy²

2020

Lecture Notes in Electrical Engineering

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Web sessions clustering using hybrid sequence alignment measure (HSAM)

Cited by 4 publications

References 18 publications

A review of alignment based similarity measures for web usage mining

A review of alignment based similarity measures for web usage mining

A behavioral sequence analyzing framework for grouping students in an e-learning system

Grouping Users Through Pair Wise Sequence Alignment and Graph Traversal Based on Web Page Navigation Behaviour

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