This paper presents a technique for generation of maps of documents targeted at placing similar documents in the same neighborhood. As a result, besides being able to group (and separate) documents by their contents, it runs at very manageable computational costs. Based on multi-dimensional projection techniques and an algorithm for projection improvement, it results in a surface map that allows the user to identify a number of important relationships between documents and sub-groups of documents via visualization and interaction. Visual attributes such as height, color, isolines and glyphs as well as aural attributes (such as pitch), help add
dimensions for integrated visual analysis. Exploration and narrowing of focus can be performed using a set of tools provided. This novel text mapping technique, named IDMAP (Interactive Document Map), is fully described in this paper. Results are compared with dimensionality reduction and cluster techniques for the same purposes. The maps are bound to support a large number of applications that rely on retrieval and examination of document collections and to complement the type of information offered by current knowledge domain visualizations.
The current availability of information many times impair the tasks of searching, browsing and analyzing information pertinent to a topic of interest. This paper presents a methodology to create a meaningful graphical representation of documents corpora targeted at supporting exploration of correlated documents. The purpose of such an approach is to produce a map from a document body on a research topic or field based on the analysis of their contents, and similarities amongst articles. The document map is generated, after text pre-processing, by projecting the data in two dimensions using Latent Semantic Indexing. The projection is followed by hierarchical
clustering to support sub-area identification. The map can be interactively explored, helping to narrow down the search for relevant articles. Tests were performed using a collection of documents pre-classified into three research subject classes: Case-Based Reasoning, Information Retrieval, and Inductive Logic Programming. The map produced was capable of separating the main areas and approaching documents by their similarity, revealing
possible topics, and identifying boundaries between them. The tool can deal with the exploration of inter-topics and intra-topic relationship and is useful in many contexts that need deciding on relevant articles to read, such as scientific research, education, and training.
KEYWORDS: Visualization, Human-machine interfaces, Visual process modeling, Visual analytics, Prototyping, Data modeling, Data analysis, Internet, Java, Computing systems
A number of different resources and a body of new technology has been empowering visualization applications. At the same time, supportive and mostly experimental techniques aimed at increasing the representation power and interpretability of complex data, such as sonification, are beginning to establish a foundation that can be used in real applications. This work presents an architecture and a corresponding prototype implementation of a visualization system that incorporates some of these research and technological aspects, such as visualization on the web, distributed visualization, and sonification. The current development of the prototype is presented, as well as its implications and planned improvements.
KEYWORDS: Reconstruction algorithms, Dentistry, Teeth, Visualization, 3D modeling, Data modeling, Visual process modeling, Computer simulations, Algorithm development, Medicine
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