The Low Income Housing Tax Credit Program is the primary federal program for producing new units of affordable housing. The program provides financial incentives to private developers to develop and operate affordable rental housing. In recent years, evidence has emerged that the program has led to clusters of subsidized housing in some cities. It is hardly surprising that some clustering would exist in a program in which the housing is constructed and owned by private developers. Despite the significant number of units produced by the program and despite the potential tendency for clustering of units built under this program, the locational patterns within the LIHTC program remain largely unexamined. Instead, most studies of the LIHTC program have focused on the national level rather than on individual cities. In contrast to previous studies, this study seeks to improve our understanding of variations in the LIHTC program across cities. The hypothesis of this study is that, because private developers produce housing in the LIHTC program and because the factors that influence private developers vary across cities, there is likely to be significant variation in the locational patterns of LIHTC developments across cities. The results of this study show, among other things, that clustering of LIHTC properties exists in the study cities, this clustering is extreme in some cases, and the clusters are associated with high poverty tracts in some cities. Given the LIHTC program's emphasis on market-driven policies and a similar emphasis in some other federal housing programs, such findings will likely be applicable to other affordable housing programs.

"To satisfy the global energy demand while accommodating the rapidly increasing consumption rate in its domestic market, Saudi Arabia must develop and implement fuel efficiency programs in many sectors. Since transportation is a major contributor to fuel consumption and emission levels, introducing Hybrid Electric Vehicles (HEV) provides a viable solution to mitigate the current problems. However, existing studies on the diffusion of innovative vehicle technologies as well as on the understanding of the vehicle ownership and consumer behavior in Saudi Arabia are sparse. To fill this knowledge gap, I have aimed at developing an in-depth knowledgebase about general vehicle ownership and HEV ownership potential in particular for Saudi Arabia in my dissertation. I have achieved the research goal through a comprehensive online questionnaire that contains three different perspectives with each contributing a chapter in my dissertation. The first perspective provides a general understanding of the vehicle owners’ behaviors by analyzing over 600 questionnaire responses. It sheds light on the vehicle ownership determinants of the respondents that currently own vehicles as well as on respondents’ future vehicle purchase plans. This research perspective reveals the importance of vehicle price and seating capacity and points out that seating capacity is not necessarily defined by the household size in Saudi Arabia. As HEV is not yet available in the Saudi market, the next perspective applies the Theory of Reasoned Action (TRA) by analyzing 847 questionnaire responses to identify factors that might drive Saudis’ intention to adopt such technology. The results indicate that, while both subjective norm and attitude are significant in explaining the intention, subjective norm has three times stronger effect on adopting HEV than attitude. The last perspective contains a three-stage analysis to help identify the profiles of the most potential HEV early adopters and increase the chance for the relevant stakeholders to reach out to an effective range of consumers. Three characteristics of such adopters are identified: at least 35 years old, part of a larger household (more than 6 people), and owning more than one vehicle. "

Data clustering is a data analysis technique that groups data based on a measure of similarity. When data is well clustered the similarities between the objects in the same group are high, while the similarities between objects in different groups are low. The data clustering technique is widely applied in a variety of areas such as bioinformatics, image segmentation and market research. This project conducted an in-depth study on data clustering with focus on density-based clustering methods. The latest density-based (CFSFDP) algorithm is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively larger distance from points with higher densities. This method has been examined, experimented, and improved. These methods (KNN-based, Gaussian Kernel-based and Iterative Gaussian Kernel-based) are applied in this project to improve (CFSFDP) density-based clustering. The methods are applied to four milestone datasets and the results are analyzed and compared.

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins.

A presente dissertação propõe novas abordagens para seleção de variáveis com vistas à formação de grupos representativos de observações. Para tanto, sugere um novo índice de importância das variáveis apoiado nos parâmetros oriundos da Análise de Componentes Principais (APC), o qual é integrado a uma sistemática do tipo forward para seleção de variáveis. A qualidade dos agrupamentos formados é medida através do Silhouette Index. Um estudo de simulação é projetado para avaliar a robustez e o desempenho da sistemática proposta em dados com diferentes níveis de correlação, ruído e número de observações a serem clusterizadas. Na sequência, é apresentada uma versão modificada da sistemática original, a qual utiliza funções kernel para remapeamento dos dados com vistas ao incremento da qualidade de clusterização e redução das variáveis retidas para formação dos agrupamentos. A versão modificada é aplicada em 3 bancos de dados da indústria química, aumentando a qualidade da clusterização medida pelo SI médio em 150% e utilizando em torno de 6% das variáveis originais. / This thesis proposes new approaches for variable selection aimed at forming representative groups of observations. For that matter, we suggest a new variable importance index based on parameters derived from the Principal Component Analysis (PCA), which is integrated to a forward procedure for variable selection. The quality of clustering procedure is assessed by the Silhouette Index. A simulation study is designed to evaluate the robustness of the proposed method on different levels of variable correlation, noise and number of observations to be clustered. Next, we modify the original method by remapping observations through kernel functions tailored to improving the clustering quality and reducing the retained variables. The modified version is applied to 3 databases related to chemical processes, increasing the quality of clustering measured by SI on average 150%, while using around 6% of the original variables.

A presente dissertação propõe novas abordagens para seleção de variáveis com vistas à formação de grupos representativos de observações. Para tanto, sugere um novo índice de importância das variáveis apoiado nos parâmetros oriundos da Análise de Componentes Principais (APC), o qual é integrado a uma sistemática do tipo forward para seleção de variáveis. A qualidade dos agrupamentos formados é medida através do Silhouette Index. Um estudo de simulação é projetado para avaliar a robustez e o desempenho da sistemática proposta em dados com diferentes níveis de correlação, ruído e número de observações a serem clusterizadas. Na sequência, é apresentada uma versão modificada da sistemática original, a qual utiliza funções kernel para remapeamento dos dados com vistas ao incremento da qualidade de clusterização e redução das variáveis retidas para formação dos agrupamentos. A versão modificada é aplicada em 3 bancos de dados da indústria química, aumentando a qualidade da clusterização medida pelo SI médio em 150% e utilizando em torno de 6% das variáveis originais. / This thesis proposes new approaches for variable selection aimed at forming representative groups of observations. For that matter, we suggest a new variable importance index based on parameters derived from the Principal Component Analysis (PCA), which is integrated to a forward procedure for variable selection. The quality of clustering procedure is assessed by the Silhouette Index. A simulation study is designed to evaluate the robustness of the proposed method on different levels of variable correlation, noise and number of observations to be clustered. Next, we modify the original method by remapping observations through kernel functions tailored to improving the clustering quality and reducing the retained variables. The modified version is applied to 3 databases related to chemical processes, increasing the quality of clustering measured by SI on average 150%, while using around 6% of the original variables.

Orientadores: Siome Klein Goldenstein, Ricardo da Silva Torres / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-09T20:33:35Z (GMT). No. of bitstreams: 1 AlmeidaJunior_JurandyGomesde_M.pdf: 4495355 bytes, checksum: 23f3f269bbf0d0e9336b8f3d53677c93 (MD5) Previous issue date: 2007 / Resumo: A evolução das tecnologias de aquisição, transmissão e armazenamento de imagens tem permitido a construção dc bancos dc imagens cada vez maiores. À medida em que cresce o volume de imagens nessas coleções, cresce também o intcresse por sistemas capazes de recuperar essas imagens. Essa tarefa tcem sido endereçada pelos sistemas de recuperação de imagens por conteúdo. Nesses sistemas, o conteúdo de uma imagem é descrito a partir de suas características visuais de baixo nível, tais como cor, forma e textura. Um sistema de recuperação de imagens por conteúdo idcal deve ser eficaz e eficiente. A eficácia é resultado de representações abstratas das imagens. Em geral, os métodos que realizam esse processo normalmente falham na presença de diferentes condições de iluminação, oclusão e foco. A eficiência, por outro lado, é resultado da organização dada à essas representações. Em geral, os métodos de agrupamento constituem uma das técnicas mais úteis para diminuir o espaço de busca e acelerar o processamento de uma consulta. Para endereçar a eficácia, este trabalho apresenta o 81FT -Texton, um método capaz de incorporar informações sobre iluminação, oclusão e foco nas características visuais de baixo nível. Esse método baseia-se na distribuição discreta de características invariantes locais e em propriedades de baixo nível das imagens. Em relação às questões de eficiência, este trabalho apresenta o DAH-Cluster, um novo paradigma de agrupamento aplicado à recuperação de imagens por conteúdo. Esse método combina características dos paradigmas hierárquicos divisivo e aglomerativo. Além disso, o DAH-Cluster introduz um novo conceito; chamado fator de reagrupamento, que permite agrupar elementos similares que seriam separados pelos paradigmas tradicionais. Experimentos mostram que a combinação dessas técnicas permite a criação de um mecanismo robusto de recuperação de imagens por conteúdo, atingindo resultados mais eficazes e mais eficientes que as abordagens tradicionais descritas na literatura. As principais contribuições deste trabalho são: (1) um novo método para recuperação de imagens capaz de incorporar informações sobre iluminação, oclusão e foco nas características visuais de baixo nível; e (2) um novo paradigma de agrupamento de dados que pode ser aplicado à recuperação de informação / Abstract: Advances in data storage, data transmission, and image acquisition have enabled the creation of large images datasets. This has spurred great interest for systems that are ablc to efficicntly rctricve images from these collections. This task has been addressed by thc so-called Content-Based Image Retrieval (CBIR) systems. ln these systems, image content is represented by their low-level features, such as color, shape, and texture. An ideal CBIR system should be effective and efficient. Effectiveness is achieved from image's abstract representations. ln general, traditional approaches for this process often fail in presence of different illumination, occlusion, and viewpoint conditions. Efficiency, on the other hand, is achieved from the organization given for these representations. ln general, data clustering approaches are one of the most useful techniques to reduce search space and speed up query processing. To address effectiveness issues, this work presents 81FT-Texton, a new method to incorporate illumination, occlusion, and viewpoint conditions into low-level features. This approach is based on discrete distributions of local invariant features and low-level image properties. With regard to efficiency issues, this work presents DAH-Cluster, a new clustering paradigm applied to CBIR. This approach combines features from both divisive and agglomerative hierarchical clustering paradigms. ln addition, DAH-Cluster introduces a new concept, called factor of reclustering, that allows grouping similar elements that would be separated by traditional clustering paradigms. Experiments show that the combination of these techniques allows the creation of a robust CBIR mechanism, achieving more effective and efficient results than traditional approaches in literature. The main contributions of this work are: (1) a new method for image retrieval that incorporates illumination, occ1usion, and viewpoint conditions into low-level features; and (2) a new data clustering paradigm that can be applied to information retrieval tasks / Mestrado / Sistemas de Informação / Mestre em Ciência da Computação

A presente dissertação propõe novas abordagens para seleção de variáveis com vistas à formação de grupos representativos de observações. Para tanto, sugere um novo índice de importância das variáveis apoiado nos parâmetros oriundos da Análise de Componentes Principais (APC), o qual é integrado a uma sistemática do tipo forward para seleção de variáveis. A qualidade dos agrupamentos formados é medida através do Silhouette Index. Um estudo de simulação é projetado para avaliar a robustez e o desempenho da sistemática proposta em dados com diferentes níveis de correlação, ruído e número de observações a serem clusterizadas. Na sequência, é apresentada uma versão modificada da sistemática original, a qual utiliza funções kernel para remapeamento dos dados com vistas ao incremento da qualidade de clusterização e redução das variáveis retidas para formação dos agrupamentos. A versão modificada é aplicada em 3 bancos de dados da indústria química, aumentando a qualidade da clusterização medida pelo SI médio em 150% e utilizando em torno de 6% das variáveis originais. / This thesis proposes new approaches for variable selection aimed at forming representative groups of observations. For that matter, we suggest a new variable importance index based on parameters derived from the Principal Component Analysis (PCA), which is integrated to a forward procedure for variable selection. The quality of clustering procedure is assessed by the Silhouette Index. A simulation study is designed to evaluate the robustness of the proposed method on different levels of variable correlation, noise and number of observations to be clustered. Next, we modify the original method by remapping observations through kernel functions tailored to improving the clustering quality and reducing the retained variables. The modified version is applied to 3 databases related to chemical processes, increasing the quality of clustering measured by SI on average 150%, while using around 6% of the original variables.

The amount of data generated is on rise due to increased demand for fields like IoT, smart monitoring applications, etc. Data generated through such systems have many distinct characteristics like continuous data generation, evolutionary, multi-source nature, and heterogeneity. In addition, the real-world data generated in these fields is largely unlabelled. Clustering is an unsupervised learning technique used to group, analyze and interpret unlabelled data. Conventional clustering algorithms are not suitable for dealing with data having previously mentioned characteristics due to memory and computational constraints, their inability to handle concept drift, distributed location of data. Therefore novel clustering approaches capable of analyzing and interpreting evolving and/or multi-source streaming data are needed.  The thesis is focused on building evolutionary clustering algorithms for data that evolves over time. We have initially proposed an evolutionary clustering approach, entitled Split-Merge Clustering (Paper I), capable of continuously updating the generated clustering solution in the presence of new data. Through the progression of the work, new challenges have been studied and addressed. Namely, the Split-Merge Clustering algorithm has been enhanced in Paper II with new capabilities to deal with the challenges of multi-view data applications. A multi-view or multi-source data presents the studied phenomenon/system from different perspectives (views), and can reveal interesting knowledge that is not visible when only one view is considered and analyzed. This has motivated us to continue in this direction by designing two other novel multi-view data stream clustering algorithms. The algorithm proposed in Paper III improves the performance and interpretability of the algorithm proposed in Paper II. Paper IV introduces a minimum spanning tree based multi-view clustering algorithm capable of transferring knowledge between consecutive data chunks, and it is also enriched with a post-clustering pattern-labeling procedure.  The proposed and studied evolutionary clustering algorithms are evaluated on various data sets. The obtained results have demonstrated the robustness of the algorithms for modeling, analyzing, and mining evolving data streams. They are able to adequately adapt single and multi-view clustering models by continuously integrating newly arriving data.

Master´s thesis is focused on cluster analysis. Clustering has its roots in many areas, including data mining, statistics, biology and machine learning. The aim of this thesis is to elaborate a recherche of cluster analysis methods, methods for determining number of clusters and a short survey of feature selection methods for unsupervised learning. The very important part of this thesis is software realization for comparing different cluster analysis methods focused on finding optimal number of clusters and sorting data points into correct classes. The program also consists of feature selection HFS method implementation. Experimental methods validation was processed in Matlab environment. The end of master´s thesis compares success of clustering methods using data with known output classes and assesses contribution of feature selection HFS method for unsupervised learning for quality of cluster analysis.