Web User Session Clustering Using Modified K-Means Algorithm

Poornalatha, G; Raghavendra, Prakash S.

doi:10.1007/978-3-642-22714-1_26

Cited by 17 publications

(12 citation statements)

References 4 publications

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“…Secondly, as sessions are expected to be differentiated by page visit orders, the appropriate metric has to take into account this order. Thus, metrics such as Levenshtein and VLVD [25] are inappropriate as they consider the visit of pageA before B and vice versa to be the same. As a result, s 1 = ABCD and s 2 = BDCA are identical.…”

Section: Proposed Methodsmentioning

confidence: 99%

Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths

Luu¹,

Ripken²,

Forestier³

et al. 2016

Machine Learning and Data Mining in Pattern Recognition

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This work takes place in the context of conversion rate optimization by enhancing the user experience during navigation on ecommerce web sites. The requirement is to be able to segment visitors into meaningful clusters, which can then be targeted with specific call-to-actions, in order to increase the web site turnover. This paper presents an original approach, which equally combines global-and localalignment techniques (Needleman-Wunsch and Smith-Waterman) in order to automatically segment visitors according to the sequence of visited pages. Experimental results on synthetic datasets show that our approach out-performs other typically used alignment metrics, such as hybrid approaches or Dynamic Time Warping.

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Section: Proposed Methodsmentioning

confidence: 99%

Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths

Luu¹,

Ripken²,

Forestier³

et al. 2016

Machine Learning and Data Mining in Pattern Recognition

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show abstract

“…For example, Vorontsov et al (2013) applied Jaccard as a metric of positional weight matrices similarity. Poornalatha and Raghavendra (2011b) also proposed VLVD (Variable Length Vector Distance) that handles web sessions regardless of the length difference. However, both approaches quantitatively define the correspondence between sequences through simple count of common elements that are contained in sequences.…”

Section: Considering Sequences With Different Lengths Ignoring Their Element Ordermentioning

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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“…Other many studies also have attempted to tackle user clustering for understanding user's behavior and recommending other websites. The most basic method of clustering is k-means clustering [11,25]; however, this method is not appropriate for our task because each user belongs to only one cluster. Many other clustering methods that allow users to belong to multiple clusters have been proposed.…”

Section: Web Usage Miningmentioning

confidence: 99%

“…Most existing works have utilized static information such as visited webpages and dwell time on the webpage to obtain users' individual information which does not change among webpages. It can be acquired by applying machine learning techniques such as k-means [11,25] or c-means [7,21] or other clustering methods. However, assuming daily use, our interest in each webpage is always changing.…”

Section: Introductionmentioning

confidence: 99%

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

Yoshida

Higurashi²,

Maruishi

et al. 2019

Data Sci. Eng.

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The studies of browsing behavior have gained increasing attention in web analysis for providing better service. Most of the conventional approaches focus on simple indices such as average dwell time and conversion rate. These indices make similar evaluations to websites even if their features are significantly different. Moreover, such statistical indices are not sensitive to the dynamics of users' interests. In this paper, we propose a new framework for measuring a website's attractiveness that takes into account both the distribution and dynamics of users' interests. Within the framework, we define a new index for the website, called Attractiveness Factor, which evaluates the degree of users' attention. It consists of three procedures: First, we capture the transition of users' interests during browsing by solving a nonnegative matrix factorization and constrained network flow problems. To accommodate multiple types of interests of a user, we applied a soft clustering as opposed to a hard clustering to model attributes of users and websites. Second, for each website, the feature of each cluster is obtained by fitting the dwell time distribution with Weibull distribution. Finally, we calculate Attractiveness Factor of a website by applying the results of clustering and fitting. Attractiveness Factor depends on the distribution of the dwell time of users interested in the website, which reflects the change of interest of users. Numerical experiments with real web access data of Yahoo Japan News are conducted by solving extremely large-scale optimization problems. They show that Attractiveness Factor captures more exceptional information about browsing behavior more effectively than well-used indices. Attractive factors give low ratings to category pages; however, it can assign high ratings to websites that attract many people, such as hot topic news about the 2018 FIFA World Cup, Japan's new imperial era' REIWA,' and North Korea-the United States Hanoi Summit. Moreover, we demonstrate that Attractiveness Factor can detect the tendency of users' attention to each website at a given time interval of the day.

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Web User Session Clustering Using Modified K-Means Algorithm

Cited by 17 publications

References 4 publications

Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths

Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths

A review of alignment based similarity measures for web usage mining

New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ Interests

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