Name
Abstract | ||
|---|---|---|
We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0% on web sites with well-formed or slightly malformed HTML, compared with 80.1% accuracy for the baseline classifier. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-71025-7_29 | Universal Access in Ambient Intelligence Environments |
Keywords | Field | DocType |
link percentage,baseline classifier,web page,accessible browsing,web site,link structure,classifying web page,accurate method,groups link,web mediator,novel link,text character,web pages,k means clustering,technical report,indexation | k-means clustering,Normalization (statistics),Web page,Information retrieval,Computer science,Web query classification,Classifier (linguistics),Agglomerative hierarchical clustering | Conference |
Volume | ISSN | Citations |
4397 | 0302-9743 | 3 |
PageRank | References | Authors |
0.50 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin N. Waber | 1 | 182 | 14.14 |
| John J. Magee | 2 | 97 | 12.08 |
| Margrit Betke | 3 | 875 | 79.80 |