Thesis (M.S.)--Kutztown University of Pennsylvania, 2009. / Source: Masters Abstracts International, Volume: 47-05, page: 2936. Adviser: Randy Kaplan. Bibliographical references (p. 50-52)

Thesis--Auburn University, 2009. / Abstract. Includes bibliographic references (p.31-33).

published_or_final_version / Computer Science / Master / Master of Philosophy

Multi-faceted data contains different types of objects and relationships between them. With rapid growth of web-based services, multi-faceted data are increasing (e.g. Flickr, Yago, IMDB), which offers us richer information to infer users’ preferences and provide them better services. In this study, we look at two types of multi-faceted data: social tagging system and heterogeneous information network and how to improve service such as resources retrieving and classification on them. In social tagging systems, resources such as images and videos are annotated with descriptive words called tags. It has been shown that tag-based resource searching and retrieval is much more effective than content-based retrieval. With the advances in mobile technology, many resources are also geo-tagged with location information. We observe that a traditional tag (word) can carry different semantics at different locations. We study how location information can be used to help distinguish the different semantics of a resource’s tags and thus to improve retrieval accuracy. Given a search query, we propose a location-partitioning method that partitions all locations into regions such that the user query carries distinguishing semantics in each region. Based on the identified regions, we utilize location information in estimating the ranking scores of resources for the given query. These ranking scores are learned using the Bayesian Personalized Ranking (BPR) framework. Two algorithms, namely, LTD and LPITF, which apply Tucker Decomposition and Pairwise Interaction Tensor Factorization, respectively for modeling the ranking score tensor are proposed. Through experiments on real datasets, we show that LTD and LPITF outperform other tag-based resource retrieval methods. A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Meta-paths are sequences of object types. They are used to represent complex relationships between objects beyond what links in a homogeneous network capture. We study the problem of classifying objects in an HIN. We propose class-level meta-paths and study how they can be used to (1) build more accurate classifiers and (2) improve active learning in identifying objects for which training labels should be obtained. We show that class-level meta-paths and object classification exhibit interesting synergy. Our experimental results show that the use of class-level meta-paths results in very effective active learning and good classification performance in HINs. / published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

Library and information services in corporations, schools, universities, and communities capture information about their users, circulation history, resources in the collection, and search patterns (Koenig, 1985). Unfortunately, few libraries have taken advantage of these data as a way to improve customer service, manage acquisition budgets, or influence strategic decision-making about uses of information in their organizations. In this chapter, we present a global view of the data generated in libraries and the variety of decisions that those data can inform. We describe ways in which library and information managers can use data mining in their libraries, i.e. bibliomining, to understand patterns of behavior among library users and staff members and patterns of information resource use throughout the institution. The chapter examines data sources and possible applications of data mining techniques and explores the legal and ethical implications of data mining in libraries.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 153-162).

Zugl.: Diss.

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, March 2009. / Thesis Advisor(s): Kamel, Magdi. "March 2009." Description based on title screen as viewed on April 24, 2009. Author(s) subject terms: Sequential Pattern Detection, Time Series, Predicting IED Attacks, Data Mining. Includes bibliographical references (p. 77). Also available in print.

Thesis (M.S.)--University of Wyoming, 2007. / Title from PDF title page (viewed on June 16, 2008). Includes bibliographical references (p. 59-60).