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

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains viii, 108 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 80-81).

Microarrays provide biologists an opportunity to find the expression profiles of thousands of genes simultaneously. Biologists try to understand the mechanisms underlying the life processes by finding out relationships between gene-expression and their functional categories. Fusion is a software system that aids the biologists in performing microarray data analysis by providing them with both visual data exploration and data mining capabilities. Its multiple view visual framework allows the user to choose different views for different types of data. Fusion uses Proteus, an Inductive Logic Programming (ILP) rule finding algorithm to mine relationships in the microarray data. Fusion allows the user to explore the data interactively, choose biases, run the data mining algorithms and visualize the discovered rules. Fusion has the capability to smoothly switch across interactive data exploration and batch data mining modes. This optimizes the knowledge discovery process by facilitating a synergy between the interactivity and usability of visualization process with the pattern-finding abilities of ILP rule mining algorithms. Fusion was successful in helping biologists better understand the mechanisms underlying the acclimatization of certain varieties of Arabidopsis to ozone exposure. / Master of Science

Dissertation submitted for the fulfillment of the requirement for the degree of Masters in Information Technology, Department of Information Technology, Faculty of Accounting and Informatics, Durban University of Technology, 2008. / Competitive pressure on the software industry encourages organizations to examine the effectiveness of their software development and evolutionary processes. Therefore it is important that software is measured in order to improve the quality. The question is not whether we should measure software but how it should be measured. Software measurement has been in existence for over three decades and it is still in the process of becoming a mature science. The many influences of new software development technologies have led to a diverse growth in software measurement technologies which have resulted in various definitions and validation techniques. An important aspect of software measurement is the measurement of the design, which nowadays often means the measurement of object oriented design. Chidamer and Kemerer (1994) designed a metric suite for object oriented design, which has provided a new foundation for metrics and acts as a starting point for further development of the software measurement science. This study documents theoretical object oriented cohesion metrics and calculates those metrics for classes extracted from a sample of open source software packages. For each open source software package, the following data is recorded: software size, age, domain, number of developers, number of bugs, support requests, feature requests, etc. The study then tests by means of association rules which theoretical cohesion metrics are validated hypothesis: that older software is more cohesive than younger software, bigger packages is less cohesive than smaller packages, and the smaller the software program the more maintainable it is. This study attempts to validate existing theoretical object oriented cohesion metrics by mining open source software data with association rules.

Dissertation submitted for the fulfillment of the requirement for the degree of Masters in Information Technology, Department of Information Technology, Faculty of Accounting and Informatics, Durban University of Technology, 2008. / Competitive pressure on the software industry encourages organizations to examine the effectiveness of their software development and evolutionary processes. Therefore it is important that software is measured in order to improve the quality. The question is not whether we should measure software but how it should be measured. Software measurement has been in existence for over three decades and it is still in the process of becoming a mature science. The many influences of new software development technologies have led to a diverse growth in software measurement technologies which have resulted in various definitions and validation techniques. An important aspect of software measurement is the measurement of the design, which nowadays often means the measurement of object oriented design. Chidamer and Kemerer (1994) designed a metric suite for object oriented design, which has provided a new foundation for metrics and acts as a starting point for further development of the software measurement science. This study documents theoretical object oriented cohesion metrics and calculates those metrics for classes extracted from a sample of open source software packages. For each open source software package, the following data is recorded: software size, age, domain, number of developers, number of bugs, support requests, feature requests, etc. The study then tests by means of association rules which theoretical cohesion metrics are validated hypothesis: that older software is more cohesive than younger software, bigger packages is less cohesive than smaller packages, and the smaller the software program the more maintainable it is. This study attempts to validate existing theoretical object oriented cohesion metrics by mining open source software data with association rules.

Le développement rapide des techniques d’extraction d’information a permis de construire de vastes bases de connaissances généralistes. Ces bases de connaissances contiennent des millions de faits portant sur des entités du monde réel, comme des personnes, des lieux, ou des organisations. Ces faits sont accessibles aux ordinateurs, et leur permettent ainsi de “comprendre” le monde réel. Ces bases trouvent donc de nombreuses applications, notamment pour la recherche d’information, le traitement de requêtes, et le raisonnement automatique. Les nombreuses informations contenues dans les bases de connaissances peuvent également être utilisées pour découvrir des motifs intéressants et fréquents dans les données. Cette tâche, l’extraction de règles d’association, permet de comprendre la structure des données ; les règles ainsi obtenues peuvent être employées pour l’analyse de données, la prédiction, et la maintenance de données, entre autres applications. Cette thèse présente deux contributions principales. En premier lieu, nous proposons une nouvelle méthode pour l’extraction de règles d’association dans les bases de connaissances. Cette méthode s’appuie sur un modèle d’extraction qui convient particulièrement aux bases de connaissances potentiellement incomplètes, comme celles qui sont extraites à partir des données du Web. En second lieu, nous montrons que l’extraction de règles peut être utilisée sur les bases de connaissances pour effectuer de nombreuses tâches orientées vers les données. Nous étudions notamment la prédiction de faits, l’alignement de schémas, la mise en forme canonique de bases de connaissances ouvertes, et la prédiction d’annotations de complétude. / The continuous progress of information extraction (IE) techniques has led to the construction of large general-purpose knowledge bases (KBs). These KBs contain millions of computer-readable facts about real-world entities such as people, organizations and places. KBs are important nowadays because they allow computers to “understand” the real world. They are used in multiple applications in Information Retrieval, Query Answering and Automatic Reasoning, among other fields. Furthermore, the plethora of information available in today’s KBs allows for the discovery of frequent patterns in the data, a task known as rule mining. Such patterns or rules convey useful insights about the data. These rules can be used in several applications ranging from data analytics and prediction to data maintenance tasks. The contribution of this thesis is twofold : First, it proposes a method to mine rules on KBs. The method relies on a mining model tailored for potentially incomplete webextracted KBs. Second, the thesis shows the applicability of rule mining in several data-oriented tasks in KBs, namely facts prediction, schema alignment, canonicalization of (open) KBs and prediction of completeness.

Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases.

In this thesis we develop data mining techniques to analyze sleep irregularities in humans. We investigate the effects of several demographic, behavioral and emotional factors on sleep progression and on patient's susceptibility to sleep-related and other disorders. Mining is performed over subjective and objective data collected from patients visiting the UMass Medical Center and the Day Kimball Hospital for treatment. Subjective data are obtained from patient responses to questions posed in a sleep questionnaire. Objective data comprise observations and clinical measurements recorded by sleep technicians using a suite of instruments together called polysomnogram. We create suitable filters to capture significant events within sleep epochs. We propose and employ a Window-based Association Rule Mining Algorithm to discover associations among sleep progression, pathology, demographics and other factors. This algorithm is a modified and extended version of the Set-and-Sequences Association Rule Mining Algorithm developed at WPI to support the mining of association rules from complex data types. We analyze both the medical as well as the statistical significance of the associations discovered by our algorithm. We also develop predictive classification models using logistic regression and compare the results with those obtained through association rule mining.

Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.