Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 67-71). Also available on the World Wide Web.

Ph.D. (Computer Science) / The development of software systems is a relatively recent field of human endeavour. Even so, it has followed a steady progression of dominant paradigms which have incrementally improved the ease with which developers are able to express the logic and structure of their systems. The initially unstructured era of free-form spaghetti code gave way to structured programming in which the entry and exit points of functional units were well defined through the creation of abstractions such as procedures, sub-routines and functions. The problem of correctly associating data with the set of operations which are legal on this data was addressed through the concept of encapsulation with the onset of object-oriented programming. Object orientation also introduced a set of abstractions for safe code reuse through inheritance and dynamic polymorphism as well as composition/aggregation and delegation. The agent-oriented software development paradigm, when viewed as an extension of object orientation, adds the capacity of agent autonomy to an object, which allows it to select for itself which of its operations it will execute at any point in time. In addition, the separation between an agent and the environment within which it is embedded must be well defined. Agent autonomy allows for the modelling and development of loosely coupled systems with the capacity for complex emergent behaviour. The mapping of a given set of environmental percepts to an agent's operation selection defines its agent function and hence its emergent behaviour. Furthermore, agents may also be embedded into a shared environment together with other agents forming a multi-agent system. The emergent characteristics of such systems are defined not only through changes in environment state but also via agent to agent interactions. Multi-agent systems are categorised into cooperative or competitive based on whether all the agents within the system share a common goal. An argument is presented that even within cooperative multi-agent systems selfishness will emerge as a direct consequence of computational intractability. The core of the argument centres on the finite nature of the computational resources available to an agent which must be divided between the evaluation of the usefulness of other agent's knowledge and intentions towards improving the collective utility of the system and directly acting upon its own. As a direct result of the halting problem it is impossible for an agent to ascertain in general whether another agent's plans are even feasible (i.e. will result in the system reaching a goal state). As a direct consequence of such a limitation agents will in general favour their own courses of action over those of others and hence an emergent selfishness occurs even in ostensibly cooperative systems...

Due to the increasing availability of massive spatio-temporal data sets, modeling high dimensional data becomes quite challenging. A large number of research questions are rooted in identifying underlying dynamics in such spatio-temporal data. For many applications, the science suggests that the intrinsic dynamics be smooth and of low dimension. To reduce the variance of estimates and increase the computational tractability, dimension reduction is also quite necessary in the modeling procedure. In this dissertation, we propose a spectral filtering approach for dimension reduction and forecast amelioration, and apply it to multiple applications. We show the effectiveness of dimension reduction via our method and also illustrate its power for prediction in both simulation and real data examples. The resultant lower dimensional principal component series has a diagonal spectral density at each frequency whose diagonal elements are in descending order, which is not well motivated can be hard to interpret. Therefore we propose a phase-based filtering method to create principal component series with interpretable dynamics in the time domain. Our method is based on an approach of structural decomposition and phase-aligned construction in the frequency domain, identifying lower-rank dynamics and its components embedded in a high dimensional spatio-temporal system. In both our simulated examples and real data applications, we illustrate that the proposed method is able to separate and identify meaningful lower-rank movements. Benefiting from the zero-coherence property of the principal component series, we subsequently develop a predictive model for high-dimensional forecasting via lower-rank dynamics. Our modeling approach reduces multivariate modeling task to multiple univariate modeling and is flexible in combining with regularization techniques to obtain more stable estimates and improve interpretability. The simulation results and real data analysis show that our model achieves superior forecast performance compared to the class of autoregressive models.

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Data clustering is a process of grouping unlabeled objects based on the imformation describing their relationship. And it has obtained a lot of attentions in data mining for its wide applications in life. For example, in marketing, companys are interested in finding groups of customers with similar purchase behavior, which will help them to make suitable plans to gain more profits. Besides, in biology, we can make use of data clustering to distinguish planets and animals given their features. Whats more, in earthquake analysis, by clustering observed earthquake epicenters, dangerous area can be identified, it would be helpful for people to take measures to protect them from earthquake in advance. In general, there isnt one clustering algorithm which can solve all the problems. Algorithms are specifically designed to analyze different data categories. In this thesis, we study several novel numerical algorithms for data clustering mainly applied on multi-view data and tensor data. More accurate clustering result can be achieved on multi-view data by integrating information from multiple graphs. However, Most existing multi-view clustering method assume the degree of association among all the graphs are the same. One significant truth is some graphs may be strongly or weakly associated with other graphs in reality. Determining the degree of association between graphs is a key issue when clustering multi-view data. In Chapter 2, 3 and 4, we propose three different models to solve this problem. In chapter 2, a block signed matrix is constructed to integrate information in each graph with association among graphs together. Then we apply spectral clustering on it to seek different cluster structure for each graph respectively and determine the degree of association among graphs using their own cluster structure at the same time. Numerical experiments including simulations, neuron activity data and gene expression data are conducted to illustrate the state-of-art performance of algorithm in clustering and graph association. In Chapter 3, we further consider multiple graphs clustering with graph association solved by self-consistent field iterative algorithm. By using the block graph clustering framework, graphs association are considered to enhance clustering result, and then better clustering result would be used to calculate more accurate association. Self-consistent field iterative method is employed to solve this problem, and the convergence analysis is also presented. Simulations are also carried out to demonstrate the outperformance of our method. Two gene expression data are used to evaluate the effectiveness of proposed model. In Chapter 4, we formulate the multiple graphs clustering problem with the graph association as an objective function, and the graph association is considered as a term in the objective function. The proposed model can be solved efficiently by using gradient flow method. We also present its convergence analysis. Experiments on synthetic data sets and two gene expression data are given to show the efficiency in clustering and capability in graphs association. In the last three chapters, we use multiple graphs to represent the multi-view data. A key challenge is high dimensionality when the number of graphs or objects is large-scale. Moreover, tensor is another common technique to describe multi-view data. Thus tensor decomposition method can be used to learn a low-dimensional representation for high dimensional data firstly and then perform clustering efficiently, which has attract worldwide attention of researchers. In Chapter 5, we propose an orthogonal nonnegative Tucker decomposition method to decompose high-dimensional nonnegative tensor into tensor with smaller size for dimension reduction, and then perform clustering analysis. A convex relaxation algorithm of the augmented Lagrangian function is devoloped to solve the optimization problem and the convergence of the algorithm is discussed. We employ our proposed method on several real image data sets from different real world application, including face recognition, image representation and hyperspectral unmixing problem to illustrate the effectiveness of proposed algorithm.

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009. / Includes bibliographical references.