Visual Data Mining of Web Navigational Data

Chen, Jian-Zhang; Zheng, Tong; Thorne, W. Bezly; Zaı̈ane, Osmar R.; Goebel, Randy

doi:10.1109/iv.2007.123

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…A system that applies different visual metaphors for representing the structure of a Web site where Web metrics are overlaid is described in [29]. Systems and visual paradigms for correlating Web navigational data with the structure of Web sites are described in [5,6,31]. Our information visualization approach is concerned with the simultaneous display of "networks of concepts", which allows for their visual comparison.…”

Section: Related Workmentioning

confidence: 99%

A Graph Drawing Application to Web Site Traffic Analysis

Didimo¹,

Liotta²,

Romeo³

2011

JGAA

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Web site traffic analysis studies how different pages forming Web sites are accessed by their target audience over time. From a business intelligence point of view, it represents one of the key activities for many private companies and public institutions. Recent papers pointed out that Web site traffic analysis is especially useful if it focuses on the user interest into the relevant concepts described in a Web site rather than counting user accesses to the distinct pages forming the Web site. This paper extends existing measures of conceptual Web site traffic analysis and describes a new system that supports this analysis by means of graph visualization techniques. The graph drawing engine of the system is a force-directed heuristic that computes a simultaneous embedding of two suitably defined graphs, that are non-planar in general. The heuristic is especially designed to take into account two important aesthetic criteria of the drawing: crossing resolution and geodesic edge tendency. We also present some experiments and case studies to show the effectiveness of the proposed approach in practice.

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Section: Related Workmentioning

confidence: 99%

A Graph Drawing Application to Web Site Traffic Analysis

Didimo¹,

Liotta²,

Romeo³

2011

JGAA

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“…Compared to the stampede algorithmic effort for extracting frequent (condensed) patterns, only few works paid attention to visualizing association rules (Blanchard et al, 2003;Blanchard et al, 2007;Buono & Costabile, 2004;Chen et al, 2007;Couturier et al, 2005a;Ertek & Demiriz, 2006;Han & Cercone, 2000;Hofmann et al, 2000;Kuntz et al, 2000;Liu et al 1999;Liu & Salvendy, 2005;Liu & Salvendy, 2006;Singh et al, 2007;Wong et al, 1999). In (Liu & Salvendy, 2005), the authors reported a survey of current state of art on the application of visualization techniques to facilitate association rules modelling process in terms of visualization of derived rules, visualization of rule evaluation and visual interactive rule derivation.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Recently different approaches were reported to treat semi-structured data (Chen et al, 2007;Singh et al, 2007). Chen et al (Chen et al, 2007) proposed a visual data mining prototype framework to visualize and mine web usage data in order to understand web page access behaviours versus the connectivity structure. Singh et al (Singh et al, 2007) introduced a new visual mining tool for social network data mining, by integrating a previous graph mining algorithm for interactive visualization.…”

Section: Related Workmentioning

confidence: 99%

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Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules

Yahia¹,

Couturier

Hamrouni³

et al. 2009

Post-Mining of Association Rules

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Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules. These tools must be able to generate explicit knowledge and, then, to present it in an elegant way. Visualization techniques have shown to be an efficient solution to achieve such a goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction step. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both data mining steps (association rule extraction and visualization) are treated separately in a one way process. Recently different approaches have been proposed that use meta-knowledge to guide the user during the mining process. Standing at the crossroads of Data Mining and Human-Computer Interaction, those approaches present an integrated framework covering both steps of the data mining process. This chapter describes and discusses such approaches. Two approaches are described in details: the first one builds a roadmap of compact representation of association rules from which the user can explore generic bases of association rules and derive, if desired, redundant ones without information loss. The second approach clusters the set of association rules or its generic bases, and uses a fisheye view technique to help the user during the mining of association rules. Generic bases with their links or the associated clusters constitute the meta-knowledge used to guide the interactive and cooperative visualization of association rules.

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“…Visual data mining (VDM) is useful in exploratory analysis, where one has limited views and information of the data. With the recent advances in computing and storage, VDM has been used for diverse applications such as exploring geospatial data (Keim, 2002 ; Keim et al, 2004 ), internet web resource databases (Chen et al, 2007 ), and analyzing business intelligence patterns (Hao et al, 2000 ). Such an effort currently does not exist in the field of neuroimaging.…”

Section: Introductionmentioning

confidence: 99%

Interactive exploration of neuroanatomical meta-spaces

Joshi

Horn

Toga

2009

Front. Neuroinform.

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Large-archives of neuroimaging data present many opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. However, interaction with such archives tends to be driven textually, based on subject or image volume meta-data, not the actual neuroanatomical morphology itself, for which the imaging was performed to measure. What is needed is a content-driven approach for examining not only the image content itself but to explore brains that are anatomically similar, and identifying patterns embedded within entire sets of neuroimaging data. With the aim of visual navigation of large- scale neurodatabases, we introduce the concept of brain meta-spaces. The meta-space encodes pair-wise dissimilarities between all individuals in a population and shows the relationships between brains as a navigable framework for exploration. We employ multidimensional scaling (MDS) to implement meta-space processing for a new coordinate system that distributes all data points (brain surfaces) in a common frame-of-reference, with anatomically similar brain data located near each other. To navigate within this derived meta-space, we have developed a fully interactive 3D visualization environment that allows users to examine hundreds of brains simultaneously, visualize clusters of brains with similar characteristics, zoom in on particular instances, and examine the surface topology of an individual brain's surface in detail. The visualization environment not only displays the dissimilarities between brains, but also renders complete surface representations of individual brain structures, allowing an instant 3D view of the anatomies, as well as their differences. The data processing is implemented in a grid-based setting using the LONI Pipeline workflow environment. Additionally users can specify a range of baseline brain atlas spaces as the underlying scale for comparative analyses. The novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis) similarity in morphometry. We believe that the concept of brain meta-spaces has important implications for the future of how users interact with large-scale archives of primary neuroimaging data.

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Visual Data Mining of Web Navigational Data

Abstract: Abstract

Cited by 8 publications

References 19 publications

A Graph Drawing Application to Web Site Traffic Analysis

A Graph Drawing Application to Web Site Traffic Analysis

Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules

Interactive exploration of neuroanatomical meta-spaces

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