Web Pages Reordering and Clustering Based on Web Patterns

Kudělka, Miloš; Snåšel, Václav; Lehecka, Ondrej; El-Qawasmeh, Eyas; Pokorný, Jaroslav

doi:10.1007/978-3-540-77566-9_63

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…In Pattrio method we work with 24 patterns (mostly ecommerce and social domain). Pattrio method is based on analysis of technical (architechtural) and semantical attributes of solutions of the same tasks in the environment of Web, for details see [10], [13].…”

Section: Pattrio Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Web Communities Defined by Web Page Content

Kudělka¹,

Snášel²,

Horák

et al. 2008

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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In this paper we are looking for a relationship between the intent of Web pages, their architecture and the communities who take part in their usage and creation. For us, the Web page is entity carrying information about these communities. Our paper describes techniques, which can be used to extract mentioned information as well as tools usable in analysis of these information. Information about communities could be used in several ways thanks to our approach. Finally we present an experiment which proves the feasibility of our approach.

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Section: Pattrio Methodsmentioning

confidence: 99%

“…In the detailed description we can distinguish, for example, between "Discussion", "Article" or "Technical Features". We can also use more general description, such as "Something to Read" or "Menu" (see [13]). …”

Section: From Web Pages To Web Communitiesmentioning

confidence: 99%

Web Communities Defined by Web Page Content

Kudělka¹,

Snášel²,

Horák

et al. 2008

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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“…As the correspondence of in-page parallel links in this paper, [14] enhances web page classification by utilizing labels and contents information from sibling pages. Web patterns [10] are formalized descriptions of common features of objects on web pages. Each page is presented by a vector of pattern weights, which record the extent of importance of the pattern for the web page.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Web-Page Clustering via In-Page and Cross-Page Link Structures

Lin

Han

et al. 2010

Advances in Knowledge Discovery and Data Mining

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Abstract. Despite of the wide diversity of web-pages, web-pages residing in a particular organization, in most cases, are organized with semantically hierarchic structures. For example, the website of a computer science department contains pages about its people, courses and research, among which pages of people are categorized into faculty, staff and students, and pages of research diversify into different areas. Uncovering such hierarchic structures could supply users a convenient way of comprehensive navigation and accelerate other web mining tasks. In this study, we extract a similarity matrix among pages via in-page and crosspage link structures, based on which a density-based clustering algorithm is developed, which hierarchically groups densely linked webpages into semantic clusters. Our experiments show that this method is efficient and effective, and sheds light on mining and exploring web structures.

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“…In paper [13] we show the way we extract snippets from individual MicroGenres. We use these snippets in our Web application as an additional information for Web page description.…”

Section: Microgenresmentioning

confidence: 99%

Social Aspects of Web Page Contents

Kudělka

Snášel

Horák

et al. 2009

2009 International Conference on Computational Aspects of Social Networks

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In this paper we try to consider a Web page as information with social aspects. Each Web page is the result of invisible social interaction. This interaction between different groups of people translates into a certain unification of Web page creation. External signs of this unification are the features of the Web page, that meets the user's expectations. Through analysis of the features, we can obtain information that can simply describe the Web page. This simple description contains strong information about the social group the page is intended for. If the user uses this information to refine the search, then he identifies himself as a member of a certain social group. For the description of the social aspects of Web pages we use the term MicroGenre. This paper describes the fundamental concepts of MicroGenre and also illustrate experiments for the detection and usage of MicroGenres.

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Web Pages Reordering and Clustering Based on Web Patterns

Cited by 11 publications

References 12 publications

Web Communities Defined by Web Page Content

Web Communities Defined by Web Page Content

Hierarchical Web-Page Clustering via In-Page and Cross-Page Link Structures

Social Aspects of Web Page Contents

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