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2006•Unpublished venueRequires access

A Classifying Web Page Templates Model Based on Fuzzy K-Means Clustering Method

Huey-Ming Lee, Ching-Hao Mao, Yao-Jen Shih, Pin-Jen Chen, Mu-Hsiu Hsu, Jin-Shieh Su

Open publisher page 2 citations

Abstract

Thousands of web pages rapidly expand every day, and the diversifications of web templates make us difficult to extract the contents of web pages. In this study, we proposed a classifying web page templates model based on fuzzy k-means clustering method. This model can automatically collect the web pages, generate several kinds of web pages templates, provide the different kinds of web content (e.g. hyperlink, image, text) templates for users' requests. Via the proposed model, we can not only classify the web pages templates more easily and efficiently, but also extract the appropriate web information on demands conveniently.

About this research paper

What this paper is about

Thousands of web pages rapidly expand every day, and the diversifications of web templates make us difficult to extract the contents of web pages. In this study, we proposed a classifying web page templates model based on fuzzy k-means clustering method. This model can automatically collect the web pages, generate several kinds of web pages templates, provide the different kinds of web content (e.g. hyperlink, image, text) templates for users' requests. Via the proposed model, we can not only classify the web pages templates more easily and efficiently, but also extract the appropriate web information on demands conveniently.

Why it matters

OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

Thousands of web pages rapidly expand every day, and the diversifications of web templates make us difficult to extract the contents of web pages. In this study, we proposed a classifying web page templates model based on fuzzy k-means clustering method. This model can automatically collect the web pages, generate several kinds of web pages templates, provide the different kinds of web content (e.g. hyperlink, image, text) templates for users' requests. Via the proposed model, we can not only classify the web pages templates more easily and efficiently, but also extract the appropriate web information on demands conveniently.

Key concepts: Web page, Computer science, Template, Static web page, Information retrieval, Hyperlink, World Wide Web, Cluster analysis

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