Γίνετε ανασκόπηση των πιο σημαντικών προβλημάτων της Ανάλυσης Χωροθέτησης. Παρατίθονται συγκρίσεις των προβλημάτων της Ανάλυσης Χωροθέτησης με τους Αλγορίθμους Ομαδοποίησης. Ακολούθως αναγράφεται μια αριθμητική εφαρμογή μιας σύγκρισης. / This project is a review of the more important algorithms of the Locational Analysis. The main theme is the comparison of the algorithms of Location Analysis against the algorithms of Clustering.

Η εργασία ασχολείται με την ομαδοποίηση δεδομένων ανθρώπινου γονιδιόματος με την χρήση αλγόριθμων ομαδοποίοησης και νευρωνικών δικτύων για τον διαχωρισμό του δείγματος σε ομάδες με βάση το αν έχουν κάποιο είδος ασθένειας ή όχι ή για τον καθορισμό του τύπου της ασθένειας. Παρουσιάζονται κάποια πειράματα που έγιναν με την χρήση και των δύο μεθόδων. / This master thesis is an application of clustering algorithms and artificial neural networks on human dna data in order to cluster the data in groups depending on wether a person has or hasn't an illness or what type of ilness one has. The thesis shows the results of some experiments conducted using either technique (clustering, ANNs) and a combination of both.

We present a technique to identify deviating systems among a group of systems in a self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix. The compressed representation is computed with Principal Component Analysis and Kernel Principal Component Analysis. The similarity measure between two compressed representations is based on the angle between the spaces spanned by the principal components, but other methods of calculating a similarity measure are suggested as well. The subsequent deviation detection is carried out by computing the probability of each system to be observed given all the other systems. Clustering of the systems is done with hierarchical clustering and spectral clustering. The whole technique is demonstrated on four data sets of mechanical systems, two of a simulated cooling system and two of human gait. The results show its applicability on these mechanical systems.

Organizing personal collections of digital documents can be frustrating for two main reasons. First, the effort required to work with the folder system on personal computers and the possible misplacement and loss of documents. Second, the lack of effective organization and management tools for personal collections of digital documents. The research in this thesis investigated specific visualization and clustering features intended for organizing collections of documents and built in a prototype interface that was compared to a baseline interface from previous research. The results showed that those features helped users with: 1) the initial classification of documents into clusters during the supervised stage; 2) the modification of clusters; 3) the cluster labeling process; 4) the presentation of the final set of organized documents; 5) the efficiency of the organization process, and 6) achieving better accuracy in the clusters created for organizing the documents.

Within nutrigenomics, clustering using data generated by microarray gene expression profiles can be used to identify sub-populations of subjects that respond differently to a given diet intervention. The use of clustering analyses is promising in obesity-related research as personalized nutrition is gaining popularity. This thesis focuses on clustering a human subcutaneous adipose tissue gene expression data set obtained during a low-calorie diet intervention to aid in the prediction of 6-month weight loss maintenance. The aims of the study were (1) to identify the best performing clustering method for clustering samples, (2) to identify differential responders to the low-calorie diet, and (3) to identify the biological pathways affected during the low-calorie diet by weight maintainers and weight regainers. MCLUST performed the best when clustering samples using relative weight change and either fasting insulin or insulin resistance change. Furthermore, it identified differences in the regulation of pathways between weight maintainers and regainers.

Clustering is a technique used to partition unlabelled data into meaningful groups. This thesis will focus on the area of clustering called model-based clustering, where it is assumed that data arise from a finite number of subpopulations, each of which follows a known statistical distribution. The number of groups and shape of each group is unknown in advance, and thus one of the most challenging aspects of clustering is selecting these features. Cross-validation is a model selection technique which is often used in regression and classification, because it tends to choose models that predict well, and are not over-fit to the data. However, it has rarely been applied in a clustering framework. Herein, cross-validation is applied to select the number of groups and covariance structure within a family of Gaussian mixture models. Results are presented for both real and simulated data. / Ontario Graduate Scholarship Program

Subspace clustering identifies the attribute support for each cluster as well as identifying the location and number of clusters. In the most general case, attributes associated with each cluster could be unique. A multi-objective evolutionary method is proposed to identify the unique attribute support of each cluster while detecting its data instances. The proposed algorithm, Symbiotic Evolutionary Subspace Clustering (S-ESC) borrows from symbiosis in the sense that each clustering solution is defined in terms of a host, which is formed by a number of co-evolved cluster centroids (or symbionts). Symbionts define clusters and therefore attribute subspaces, whereas hosts define sets of clusters to constitute a non-degenerate clustering solution. The symbiotic representation of S-ESC is the key to making it scalable to high-dimensional datasets, while a subsampling process makes it scalable to large-scale datasets. Performance of the S-ESC algorithm was found to be robust across a common parameterization utilized throughout.

Small angle x-ray scattering (SAXS) with synchrotron radiation was used to characterize nano-sized voids in different nominally pure aluminum (Al) alloys produced by quenching. The scattering signal from nano-voids is shown to be predictable from SAXS theory, and the information related to the void population confirm past experiments and reveal new details about quench-void formation in Al. Specifically, voids were produced in 99.97 at.% to 99.9994 at.% Al alloys by infrared heating to 450 – 625 °C followed by controlled rapid quenching at 10^3 to 10^5 °C/s. For changing processing conditions, the size of voids varied between 5 to 11 nm, and the density of voids varied by over an order of magnitude. Results from SAXS were consistent with TEM observations performed on the same specimens, indicating that synchrotron SAXS can be reliably used to characterize nano-voids produced in quenched Al. Factors determined to affect voids were consistent with previous studies, except that the present nano-voids dissolved after only 3 min. at 145 °C, indicating that quenched nano-voids are less stable than previously determined. SAXS also showed that void size is sensitive to quench temperature and quench rate. The activation energies for void nucleation and growth were determined to be 0.75 ± 0.10 and 0.19 ± 0.03 eV/at., respectively, confirming that hydrogen and di-vacancies take part in nucleation and growth during quenching. It was concluded that the non-linear tail of the quench curve plays a crucial role in void formation, and that voids form when long range diffusion is inhibited. This information can be utilized to design new Al alloys that limit incipient void formation, which is detrimental to properties such as formability. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2008-04-25 15:17:30.211 / Natural Sciences and Engineering Research Council of Canada; General Motors of Canada Limited

Pervasive Developmental Disorders (PDDs) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behaviour [Str04]. Given the diversity and varying severity of PDDs, diagnostic tools attempt to identify homogeneous subtypes within PDDs. The diagnostic system Diagnostic and Statistical Manual of Mental Disorders - Fourth Edition (DSM-IV) divides PDDs into five subtypes. Several limitations have been identified with the categorical diagnostic criteria of the DSM-IV. The goal of this study is to identify putative subtypes in the multidimensional data collected from a group of patients with PDDs, by using cluster analysis. Cluster analysis is an unsupervised machine learning method. It offers a way to partition a dataset into subsets that share common patterns. We apply cluster analysis to data collected from 358 children with PDDs, and validate the resulting clusters. Notably, there are many cluster analysis algorithms to choose from, each making certain assumptions about the data and about how clusters should be formed. A way to arrive at a meaningful solution is to use consensus clustering to integrate results from several clustering attempts that form a cluster ensemble into a unified consensus answer, and can provide robust and accurate results [TJPA05]. In this study, using cluster analysis, cluster validation, and consensus clustering, we identify four clusters that are similar to – and further refine  three of the five subtypes defined in the DSM-IV. This study thus confirms the existence of these three subtypes among patients with PDDs. / Thesis (Master, Computing) -- Queen's University, 2007-11-15 23:34:36.62 / OGS, QGA

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