Thesis (MCIS)--AUT University, 2009. / Includes bibliographical references. Also held in print (72 leaves : ill. ; 30 cm.) in the Archive at the City Campus (T 006.312 VIT)

Thesis (M. Phil.)--Chinese University of Hong Kong, 1998. / Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed on Jan 23, 2007). PDF text: 127 p. : ill. (some col.) ; 1.29Mb. UMI publication number: AAT 3216105. Includes bibliographical references. Also available in microfilm and microfiche format.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).

Adverse Drug Reactions (ADRs) are a major cause of morbidity and mortality worldwide. Clinical trials, which are extremely costly, human labor intensive and specific to controlled human subjects, are ineffective to uncover all ADRs related to a drug. There is thus a growing need of computing-supported methods facilitating the automated detection of drugs-related ADRs from large reports data sets; especially ADRs that left undiscovered during clinical trials but later arise due to drug-drug interactions or prolonged usage. For this purpose, big data sets available through drug-surveillance programs and social media provide a wealth of longevity information and thus a huge opportunity. In this research, we thus design a system using machine learning techniques to discover severe unknown ADRs triggered by a combination of drugs, also known as drug-drug-interaction. Our proposed Multi-drug Adverse Reaction Analytics System (MARAS) adopts and adapts an association rule mining-based methodology by incorporating contextual information to detect, highlight and visualize interesting drug combinations that are strongly associated with a set of ADRs. MARAS extracts non-spurious associations that are true representations of the combination of drugs taken and reported by patients. We demonstrate the utility of MARAS via case studies from the medical literature, and the usability of the MARAS system via a user study using real world medical data extracted from the FDA Adverse Event Reporting System (FAERS).

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.

Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our second approach dynamically evaluates association rules according to a composite and collective effect of multiple measures. It interactively snapshots the end user’s domain- dependent requirements in evaluating association rules. In essence, our approach uses neural networks along with back-propagation learning to capture the relative importance of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches. / viii, 85 leaves : ill. ; 29 cm

From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.

From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.

Thesis (M.S.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.