Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-04-27T11:59:30Z No. of bitstreams: 2 Dissertação - Max Gontijo de Oliveira - 2012.pdf: 3940401 bytes, checksum: 9d69259096bb8d7b7239f7eb20579d8d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-04-27T12:01:50Z (GMT) No. of bitstreams: 2 Dissertação - Max Gontijo de Oliveira - 2012.pdf: 3940401 bytes, checksum: 9d69259096bb8d7b7239f7eb20579d8d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-04-27T12:01:50Z (GMT). No. of bitstreams: 2 Dissertação - Max Gontijo de Oliveira - 2012.pdf: 3940401 bytes, checksum: 9d69259096bb8d7b7239f7eb20579d8d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2012-10-08 / Several organizations need to solve the problem of locate and allocate facilities within a geographic area. There are location/allocation problems in various situations, like the distribution of police cars, ambulances, taxi drivers, bus stops among other numerous situations where the location of such facilities is strategic for organization. In location/allocation problems, usually is necessary allocate each demand point to the closest facility. So, each facility will be located in the center of demand points, considering the demand as weight. However, the majority of the real location problems have capacity constraint. Therefore, each facility has a certain capacity based on the type of demand. Facility location problems can be continuous or discrete. In continuous problems (also called Weber problem with multiple sources), any point in the plane is a potential site for the instalation of the facility. There are several approaches for working with continuous models. Furthermore, there are many others works approaches presenting models with capacity constraint. But most of these approaches turns the continous model to a discrete model. The objective of this work thesis is to present an approach to distribution of facilities in instances of the capacitated facility location problem. A case study will be presented with the purpose of evaluating the results. / Diversas organizações precisam lidar com o problema de localizar e alocar facilidades em uma região geográfica. Problemas de localização e alocação podem ser vistos, por exemplo, na distribuição de viaturas policiais, ambulâncias, viaturas de contenção de falhas em redes elétricas, taxistas, pontos de ônibus dentre outras inúmeras situações onde a localização de tais facilidades é um fator estratégico para a organização. Em problemas de localização/alocação de facilidades, geralmente aloca-se cada ponto de demanda à facilidade mais próxima e, localiza-se essa facilidade no centro dos pontos de demanda, considerando o valor da demanda como peso nessa distância. Entretanto, comumente, problemas reais de localização de facilidades possuem restrição de capacidade. Assim, cada facilidade possui uma certa capacidade em função do tipo de demanda. Problemas de localização de facilidades podem ser contínuos ou discretos. Em problemas contínuos (também chamados de problema de Weber com múltiplas fontes), qualquer ponto no plano é um potencial local para se instalar uma facilidade. Existem várias abordagens para trabalhar com modelos contínuos e outras tantas para trabalhar com modelos com restrição de capacidade, mas a maioria dessas abordagens realiza uma discretização do modelo. Assim, o objetivo desse trabalho é apresentar uma abordagem para gerar boas distribuições de facilidades para o problema de localização/alocação contínuo com restrição de capacidade. Um caso de estudo será apresentado com a finalidade de avaliar os resultados obtidos.

A pivotal moment in machine learning research was the creation of an important new research area, known as Ensemble Learning. In this work, we argue that ensembles are a very general concept, and though they have been widely used, they can be applied in more situations than they have been to date. Rather than using them only to combine the output of an algorithm, we can apply them to decisions made inside the algorithm itself, during the learning step. We call this approach Inner Ensembles. The motivation to develop Inner Ensembles was the opportunity to produce models with the similar advantages as regular ensembles, accuracy and stability for example, plus additional advantages such as comprehensibility, simplicity, rapid classification and small memory footprint. The main contribution of this work is to demonstrate how broadly this idea can be applied, and highlight its potential impact on all types of algorithms. To support our claim, we first provide a general guideline for applying Inner Ensembles to different algorithms. Then, using this framework, we apply them to two categories of learning methods: supervised and un-supervised. For the former we chose Bayesian network, and for the latter K-Means clustering. Our results show that 1) the overall performance of Inner Ensembles is significantly better than the original methods, and 2) Inner Ensembles provide similar performance improvements as regular ensembles.

No description available.

No description available.

Master of Science / Department of Computing and Information Sciences / David A. Gustafson William Hsu / In this study, we identify a new method of tracking motion over a sequence of images using feature point clusters. We identify and implement a system that takes as input a sequence of images and generates clusters of SIFT features using the K-Means clustering algorithm. Every time the system processes an image it compares each new cluster to the clusters of previous images, which it stores in a local cache. When at least 25% of the SIFT features that compose a cluster match a cluster in the local cache, the system uses the centroid of both clusters in order to determine the direction of travel. To establish a direction of travel, we calculate the slope of the line connecting the centroid of two clusters relative to their Cartesian coordinates in the secondary image. In an experiment using a P3-AT mobile robotic agent equipped with a digital camera, the system receives and processes a sequence of eight images. Experimental results show that the system is able to identify and track the motion of objects using SIFT feature clusters more efficiently when applying spatial outlier detection prior to generating clusters.

Data clustering involves partitioning data points into clusters where data points within the same cluster have high similarity, but are dissimilar to the data points in other clusters. The k-means algorithm is among the most extensively used clustering techniques. Genetic algorithms (GA) have been successfully used to evolve successive generations of cluster centers. The primary goal of this research was to develop improved GA-based methods for center selection in k-means by using heuristic methods to improve the overall fitness of the initial population of chromosomes along with crowding techniques to avoid premature convergence. Prior to this research, no rigorous systematic examination of the use of heuristics and crowding methods in this domain had been performed. The evaluation included computational experiments involving repeated runs of the genetic algorithm in which values that affect heuristics or crowding were systematically varied and the results analyzed. Genetic algorithm performance under the various configurations was analyzed based upon (1) the fitness of the partitions produced, and by (2) the overall time it took the GA to converge to good solutions. Two heuristic methods for initial center seeding were tested: Density and Separation. Two crowding techniques were evaluated on their ability to prevent premature convergence: Deterministic and Parent Favored Hybrid local tournament selection. Based on the experiment results, the Density method provides no significant advantage over random seeding either in discovering quality partitions or in more quickly evolving better partitions. The Separation method appears to result in an increased probability of the genetic algorithm finding slightly better partitions in slightly fewer generations, and to more quickly converge to quality partitions. Both local tournament selection techniques consistently allowed the genetic algorithm to find better quality partitions than roulette-wheel sampling. Deterministic selection consistently found better quality partitions in fewer generations than Parent Favored Hybrid. The combination of Separation center seeding and Deterministic selection performed better than any other combination, achieving the lowest mean best SSE value more than twice as often as any other combination. On all 28 benchmark problem instances, the combination identified solutions that were at least as good as any identified by extant methods.

R-medžių analizė, taikant juos duomenų gavybos algoritmams. / Analysis of R-trees for Data Mining Algorithms.

La minería de datos distribuida está contemplada en el campo de la investigación que implica la aplicación del proceso de extracción de conocimiento sobre grandes volúmenes de información almacenados en bases de datos distribuidas. Las organizaciones modernas requieren de herramientas que realicen tareas de predicción, pronósticos, clasificación entre otros y en línea, sobre sus bases de datos que se ubican en diferentes nodos interconectados a través de internet, de manera que les permita mejorar la calidad de sus servicios. En ese contexto, el presente trabajo realiza una revisión bibliográfica de las técnicas clustering k-means, elabora una propuesta concreta, desarrolla un prototipo de aplicación y concluye fundamentando los beneficios que obtendrían las organizaciones con su implementación.

Clustering is a mathematical method of data analysis which identifies trends in data by efficiently separating data into a specified number of clusters so is incredibly useful and widely applicable for questions of interrelatedness of data. Two methods of clustering are considered here. K-means clustering defines clusters in relation to the centroid, or center, of a cluster. Spectral clustering establishes connections between all of the data points to be clustered, then eliminates those connections that link dissimilar points. This is represented as an eigenvector problem where the solution is given by the eigenvectors of the Normalized Graph Laplacian. Spectral clustering establishes groups so that the similarity between points of the same cluster is stronger than similarity between different clusters. K-means and spectral clustering are used to analyze adolescent data from the 2009 California Health Interview Survey. Differences were observed between the results of the clustering methods on 3294 individuals and 22 health-related attributes. K-means clustered the adolescents by exercise, poverty, and variables related to psychological health while spectral clustering groups were informed by smoking, alcohol use, low exercise, psychological distress, low parental involvement, and poverty. We posit some guesses as to this difference, observe characteristics of the clustering methods, and comment on the viability of spectral clustering on healthcare data.

With the proliferation of wireless technologies, wireless Internet access in public places will become a necessity in the near future. In outdoor areas, where the base stations are sparsely distributed, mobile users at the edge of the network communicate with the base station at a very low rate and thus waste network resources. To solve this problem, one of the previously taken approaches was the use of relay stations to improve the throughput of the network. In this work, we take this approach to the next level by updating the positions of the relays according to the particular distribution of the users at certain time instants. By comparing the proposed scheme to fixed relay placement strategies, we show that the former has 15-60% performance improvement over the latter, in terms of the average SNR of the network.