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Content Provider | ACM Digital Library |
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Author | Kerne, Andruid Mistrot, J. Michael Koh, Eunyee Sundaram, Vikram |
Abstract | This paper presents an iterative method for generative semantic clustering of related information elements in spatial hypertext documents. The goal is to automatically organize them in ways that are meaningful to the user. We consider a process in which elements are gradually added to a spatial hypertext. The method for generating meaningful layout is based on a quantitative model that measures and represents the mutual relatedness between each new element and those already in the document. The measurement is based on attributes such as metadata, term vectors, user interest expressions, and document locations. We call this model relatedness potential, because it represents how much the new element is related and thus attracted to existing elements as a vector field across the space. Using this field as a gradient potential, the new element will be placed near the most attracted elements, forming clusters of related elements. The relative magnitude of contribution of attributes to relatedness potential can be controlled through an interactive interface.Unlike prior clustering methods such as k-means and self-organizing-maps, relatedness potential works well in iterative systems, in which the collection of elements is not defined a priori. Further, users can invoke relatedness potential to re-cluster elements, as they engage in on-the-fly provisional acts of direct manipulation reorganization and latching of a few most significant elements. A preliminary study indicates that users find this method generates spatial hypertext documents that are easier to read. |
Starting Page | 84 |
Ending Page | 93 |
Page Count | 10 |
File Format | |
ISBN | 1595932402 |
DOI | 10.1145/1096601.1096624 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2005-11-02 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Document layout Spatial hypertext Generative hypermedia Mixed-initiatives Clustering Collections Information triage |
Content Type | Text |
Resource Type | Article |
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