Thesis (M.S.)--University of Florida, 2002. / Title from title page of source document. Document formatted into pages; contains ix, 64 p.; also contains graphics. Includes vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 176-190).

Thesis (Ph. D.)--Ohio State University, 1992. / Includes bibliographical references (leaves 116-119). Available online via OhioLINK's ETD Center.

Thesis (M.C.S.)--Carleton University, 1992. / Also available in electronic format on the Internet.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2010. / Title from PDF title page (viewed on Jul. 14, 2010). Includes bibliographical references.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2006. / Title from PDF title page (viewed on Dec. 13, 2006). Includes bibliographical references.

Thesis (Ph. D.)--Virginia Commonwealth University, 2006. / Prepared for: Dept. of Information Systems. Bibliography: leaves 142-148. Also available online.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2006. / Title from PDF title page (viewed on Sept. 7, 2006). Includes bibliographical references.

The initial project was funded by an Australian Research Council (ARC) Research Infrastructure Equipment and Facilities Scheme grant. The model was developed by seven Australian universities in association with the Council of Australian University Librarians (CAUL), with the University of New South Wales as the lead institution. Description based on: Feb. 26, 2001 update ; title from homepage (viewed 5 June 2001) Available in HTML and PDF file formats. ADT is a project dedicated to establishing a distributed database of digital versions of theses produced by postgraduate research students at Australian universities. The theses of member institutions are digitised and made available as part of the deposit process. A selected number of existing theses which are frequently requested are also digitised. Users may search the database or browse the theses by institution. The retrieval is enhanced by the inclusion of metadata tags in the documents. Mode of access: Internet. System requirements: Acrobat reader required to view/print PDF files.

Many frequent pattern mining algorithms operate on precise data, where each data point is an exact accounting of a phenomena (e.g., I have exactly two sisters). Alas, reasoning this way is a simplification for many real world observations. Measurements, predictions, environmental factors, human error, &ct. all introduce a degree of uncertainty into the mix. Tree-based frequent pattern mining algorithms such as FP-growth are particularly efficient due to their compact in-memory representations of the input database, but their uncertain extensions can require many more tree nodes. I propose new algorithms with tightened upper bounds to expected support, Tube-S and Tube-P, which mine frequent patterns from uncertain data. Extensive experimentation and analysis on datasets with different probability distributions are undertaken that show the tightness of my bounds in different situations. / February 2016