Thesis (M.S.)--University of Iowa, 2007. / Supervisor: Andrew Kusiak. Includes bibliographical references (leaves 60-62).

Thesis (M.S.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September ) Vita. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 158-168).

Thesis (M.S.) -- Central Connecticut State University, 2007. / Thesis advisor: Daniel Larose "... in partial fulfillment of the requirements for the degree of Master of Science in data Mining." Includes bibliographical references (leaves 53-55). Also available via the World Wide Web.

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

Doctor of Philosophy / Department of Mathematics / Andrew Bennett / Many students have difficulty performing well in Calculus 1. Since Calculus 1 is often the first math course that people take in college, these difficulties can set a precedent of failure for these students. Using tools from data mining and interviews with Precalculus and Calculus 1 students, this work seeks to identify the different types of students in Calculus 1, determine which students are at risk for failure, and to study how intervention can help them succeed both in mathematics and in their college careers.

Com o advento da Internet, as empresas puderam mostrar-se para o mundo. A possibilidade de colocar um negócio na World Wide Web (WWW) criou um novo tipo de dado que as empresas podem utilizar para melhorar ainda mais seu conhecimento sobre o mercado: a sequência de cliques que um usuário efetua em um site. Esse dado pode ser armazenado em uma espécie de Data Warehouse para ser analisado com técnicas de descoberta de conhecimento em bases de dados. Assim, há a necessidade de se realizar pesquisas para mostrar como retirar conhecimento a partir dessas sequências de cliques. Neste trabalho são discutidas e analisadas algumas das técnicas utilizadas para atingir esse objetivo. é proposta uma ferramenta onde os dados dessas sequências de cliques são mapeadas para o formato atributo-valor utilizado pelo Sistema Discover, um sistema sendo desenvolvindo em nosso Laboratório para o planejamento e execução de experimentos relacionados aos algoritmos de aprendizado utilizados durante a fase de Mineração de Dados do processo de descoberta de conhecimento em bases de dados. Ainda, é proposta a utilização do sistema de Programação Lógica Indutiva chamado Progol para extrair conhecimento relacional das sessões de sequências de cliques que caracterizam a interação de usuários com as páginas visitadas no site. Experimentos iniciais com a utilização de uma sequência de cliques real foram realizados usando Progol e algumas das facilidades já implementadas pelo Sistema Discover.

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

Managers the world over complain that they are overwhelmed by the amount of data available to them, but that they are unable to make any sense of this data. The changing business environment and the fact that customers are becoming more and more demanding highlight the need for organisations to be able to adapt faster and more effectively to those changes. Data mining developed as a direct result of the natural evolution of information technology. The increased organisational use of computer based systems has resulted in the accumulation of vast amounts of data, and the need for decision makers to have efficient access to knowledge, and not only data, has resulted in more and more organisations adopting the use of data mining. The promise of data mining is to return the focus of large, impersonal organisations to serving their customers better and to providing more efficient business processes. Indeed, for some organisations data mining offers the potential for gaining a competitive advantage, but for others it has become a matter of survival. The literature is filled with examples of the successful application of data mining, not only to specific business functions, but also in specific industries. Undoubtedly, certain industries, such as those dealing with huge amounts of data, and those exposed to many diverse customers, stand to benefit more from data mining than others. iii The benefits, associated with data mining, for organisations, individuals and society as a whole, far exceed its drawbacks, but the biggest issue facing organisations that want to employ data mining, is its cost. The other drawbacks of data mining relate to the threat that it poses to privacy, and any data mining effort must not only be done within the framework of the relevant laws, but must also be done in an ethical manner. Although data mining is probably beyond the financial ability of most organisations, its main principle, the fact that there might be value in organisational data, should not be forgotten. Organisations must endeavour to treat their data with the same respect it has for all its other corporate assets. / Mr. C. Scheepers