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Financial market predictions using Web mining approaches /Ma, Yao. January 2009 (has links)
Includes bibliographical references (p. 62-67).
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OLAP on sequence dataChui, Chun-kit, 崔俊傑 January 2010 (has links)
published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy
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Techniques in data stream miningTong, Suk-man, Ivy., 湯淑敏. January 2005 (has links)
published_or_final_version / abstract / Computer Science / Master / Master of Philosophy
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Automaticially Detecting Deceptive Criminal IdentitiesWang, Gang, Chen, Hsinchun, Atabakhsh, Homa 03 1900 (has links)
Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona / Fear about identity verification reached new heights since the
terrorist attacks on Sept. 11, 2001, with national security issues
related to detecting identity deception attracting more interest
than ever before. Identity deception is an intentional falsification
of identity in order to deter investigations. Conventional investigation
methods run into difficulty when dealing with criminals
who use deceptive or fraudulent identities, as the FBI discovered
when trying to determine the true identities of 19 hijackers
involved in the attacks. Besides its use in post-event investigation,
the ability to validate identity can also be used as a tool to prevent
future tragedies. Here, we focus on uncovering patterns
of criminal identity deception
based on actual criminal records and
suggest an algorithmic approach to
revealing deceptive identities.
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The Bibliomining Process: Data Warehousing and Data Mining for Library Decision-MakingNicholson, Scott January 2003 (has links)
The goal of this brief article is to explain the bibliomining process. Emphasis is placed on data warehousing and patron privacy issues because they are required before anything else can begin. It is essential to capture our data-based institutional records while still protecting the privacy of users. By using a data warehouse, both goals can be met. Once the data warehouse is in place, the library can use reporting and exploration tools to gain a more thorough knowledge of their user communities and resource utilization.
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Discovering and summarizing email conversationsZhou, Xiaodong 05 1900 (has links)
With the ever increasing popularity of emails, it is very common nowadays that people discuss specific issues, events or tasks among a group of people by emails. Those discussions can be viewed as conversations via emails and are valuable for the user as a personal information repository. For instance, in 10 minutes before a meeting, a user may want to quickly go through a previous discussion via emails that is going to be discussed in the meeting soon. In this case, rather than reading each individual email one by one, it is preferable to read a concise summary of the previous discussion with major information summarized. In this thesis, we study the problem of discovering and summarizing email conversations. We believe that our work can greatly support users with their email folders. However, the characteristics of email conversations, e.g., lack of synchronization, conversational structure and informal writing style, make this task particularly challenging. In this thesis, we tackle this task by considering the following aspects: discovering emails in one conversation, capturing the conversation structure and summarizing the email conversation. We first study how to discover all emails belonging to one conversation. Specifically, we study the hidden email problem, which is important for email summarization and other applications but has not been studied before. We propose a framework to discover and regenerate hidden emails. The empirical evaluation shows that this framework is accurate and scalable to large folders. Second, we build a fragment quotation graph to capture email conversations. The hidden emails belonging to each conversation are also included into the corresponding graph. Based on the quotation graph, we develop a novel email conversation summarizer, ClueWordSummarizer. The comparison with a state-of-the-art email summarizer as well as with a popular multi-document summarizer shows that ClueWordSummarizer obtains a higher accuracy in most cases. Furthermore, to address the characteristics of email conversations, we study several ways to improve the ClueWordSummarizer by considering more lexical features. The experiments show that many of those improvements can significantly increase the accuracy especially the subjective words and phrases.
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Explicating a Biological Basis for Chronic Fatigue SyndromeAbou-Gouda, Samar A. 18 December 2007 (has links)
In the absence of clinical markers for Chronic Fatigue Syndrome (CFS), research
to find a biological basis for it is still open. Many data-mining techniques have
been widely employed to analyze biomedical data describing different aspects of CFS.
However, the inconsistency of the results of these studies reflect the uncertainty in
regards to the real basis of this disease. In this thesis, we show that CFS has a
biological basis that is detectable in gene expression data better than blood profile
and Single Nucleotide Polymorphism (SNP) data. Using random forests, the analysis
of gene expression data achieves a prediction accuracy of approximately 89%. We also
identify sets of differentially expressed candidate genes that might contribute to CFS.
We show that the integration of data spanning multiple levels of the biological scale
might reveal further insights into the understanding of CFS. Using integrated data,
we achieve a prediction accuracy of approximately 91%. We find that Singular Value
Decomposition (SVD) is a useful technique to visualize the performance of random
forests. / Thesis (Master, Computing) -- Queen's University, 2007-12-11 12:15:40.096
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Supervised and unsupervised machine learning for pattern recognition and time series prediction /Bean, Kathryn Brenda, January 2008 (has links)
Thesis (Ph.D.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 78-81)
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Weighted clustering ensemblesAl-Razgan, Muna Saleh. January 2008 (has links)
Thesis (Ph.D.)--George Mason University, 2008. / Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.
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Multiple additive regression trees : a methodology for predictive data mining for fraud detection /Monteiro, António S. January 2002 (has links) (PDF)
Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2002. / Thesis advisor(s): Lyn R. Whitaker, Samuel E. Buttrey. Includes bibliographical references (p. 89-91). Also available online.
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