Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: XML; path expression; axis; order; indexing; inlined; compression; XPath; lossless. Includes bibliographical references (p. 45-46).

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Valdis Berzins, Paul E. Young. Includes bibliographical references (p. 109). Also available online.

Thesis (M.S. in Software Engineering)--Naval Postgraduate School, March 2004. / Thesis advisor(s): Man-Tak Shing. Includes bibliographical references (p. 101-102). Also available online.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains iii, 47 p. Vita. Includes abstract. Includes bibliographical references (p. 36-37).

Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 2003. / Thesis advisor(s): Valdis Berzins, Paul Young. Includes bibliographical references (p. 127-130). Also available online.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: XML; views; updates; Rainbow. Includes bibliographical references (p. 94-96).

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: XML; XQuery; schema and data evolution. Includes bibliographical references (p. 69-72).

published_or_final_version / abstract / Geography / Master / Master of Geographic Information System

Nowadays, academic researchers maintain a personal library of papers, which they would like to organize based on their needs, e.g., research, projects, or courseware. Clustering techniques are often employed to achieve this goal by grouping the document collection into different topics. Unsupervised clustering does not require any user effort but only produces one universal output with which users may not be satisfied. Therefore, document clustering needs user input for guidance to generate personalized clusters for different users. Semi-supervised clustering incorporates prior information and has the potential to produce customized clusters. Traditional semi-supervised clustering is based on user supervision in the form of labeled instances or pairwise instance constraints. However, alternative forms of user supervision exist such as labeling features. For document clustering, document supervision involves labeling documents while feature supervision involves labeling features. Their joint of use has been called dual supervision. In this thesis, we first explore and propose a framework to use feature supervision for interactive feature selection by indicating whether a feature is useful for clustering. Second, we enhance the semi-supervised clustering with feature supervision using feature reweighting. Third, we propose a unified framework to combine document supervision and feature supervision through seeding. The newly proposed algorithms are evaluated using oracles and demonstrated to be more helpful in producing better clusters matching a single user's point of view than document clustering without any supervision and with only document supervision. Finally, we conduct a user study to confirm that different users have different understandings of the same document collection and prefer personalized clusters. At the same time, we demonstrate that document clustering with dual supervision is able to produce good personalized clusters even with noisy user input. Dual supervision is also demonstrated to be more effective in personalized clustering than no supervision or any single supervision. We also analyze users' behaviors during the user study and present suggestions for the design of document management software.

Organizing personal collections of digital documents can be frustrating for two main reasons. First, the effort required to work with the folder system on personal computers and the possible misplacement and loss of documents. Second, the lack of effective organization and management tools for personal collections of digital documents. The research in this thesis investigated specific visualization and clustering features intended for organizing collections of documents and built in a prototype interface that was compared to a baseline interface from previous research. The results showed that those features helped users with: 1) the initial classification of documents into clusters during the supervised stage; 2) the modification of clusters; 3) the cluster labeling process; 4) the presentation of the final set of organized documents; 5) the efficiency of the organization process, and 6) achieving better accuracy in the clusters created for organizing the documents.