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Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectrometry provide huge amounts of data per measurement and challenge traditional analyses. New strategies of data processing, visualization and functional analysis are inevitable. This thesis presents an approach which applies a machine learning technique known as self organizing maps (SOMs). SOMs enable the parallel sample- and feature-centered view of molecular phenotypes combined with strong visualization and second-level analysis capabilities. We developed a comprehensive analysis and visualization pipeline based on SOMs. The unsupervised SOM mapping projects the initially high number of features, such as gene expression profiles, to meta-feature clusters of similar and hence potentially co-regulated single features. This reduction of dimension is attained by the re-weighting of primary information and does not entail a loss of primary information in contrast to simple filtering approaches. The meta-data provided by the SOM algorithm is visualized in terms of intuitive mosaic portraits. Sample-specific and common properties shared between samples emerge as a handful of localized spots in the portraits collecting groups of co-regulated and co-expressed meta-features. This characteristic color patterns reflect the data landscape of each sample and promote immediate identification of (meta-)features of interest. It will be demonstrated that SOM portraits transform large and heterogeneous sets of molecular biological data into an atlas of sample-specific texture maps which can be directly compared in terms of similarities and dissimilarities. Spot-clusters of correlated meta-features can be extracted from the SOM portraits in a subsequent step of aggregation. This spot-clustering effectively enables reduction of the dimensionality of the data in two subsequent steps towards a handful of signature modules in an unsupervised fashion. Furthermore we demonstrate that analysis techniques provide enhanced resolution if applied to the meta-features. The improved discrimination power of meta-features in downstream analyses such as hierarchical clustering, independent component analysis or pairwise correlation analysis is ascribed to essentially two facts: Firstly, the set of meta-features better represents the diversity of patterns and modes inherent in the data and secondly, it also possesses the better signal-to-noise characteristics as a comparable collection of single features. Additionally to the pattern-driven feature selection in the SOM portraits, we apply statistical measures to detect significantly differential features between sample classes. Implementation of scoring measurements supplements the basal SOM algorithm. Further, two variants of functional enrichment analyses are introduced which link sample specific patterns of the meta-feature landscape with biological knowledge and support functional interpretation of the data based on the ‘guilt by association’ principle. Finally, case studies selected from different ‘OMIC’ realms are presented in this thesis. In particular, molecular phenotype data derived from expression microarrays (mRNA, miRNA), sequencing (DNA methylation, histone modification patterns) or mass spectrometry (proteome), and also genotype data (SNP-microarrays) is analyzed. It is shown that the SOM analysis pipeline implies strong application capabilities and covers a broad range of potential purposes ranging from time series and treatment-vs.-control experiments to discrimination of samples according to genotypic, phenotypic or taxonomic classifications.

In this thesis work, we propose a Map-Reduce variant of the Growing Hierarchical Self Organizing Map (GHSOM) called MR-GHSOM, which is capable of handling mixed attribute datasets of massive size. The Self Organizing Map (SOM) has proved to be a useful unsupervised data analysis algorithm. It projects a high dimensional data onto a lower dimensional grid of neurons. However, the SOM has some limitations owing to its static structure and the incapability to mirror the hierarchical relations in the data. The GHSOM overcomes these shortcomings of the SOM by providing a dynamic structure that adapts its shape according to the input data. It is capable of growing dynamically in terms of the size of the individual neuron layers to represent data at the desired granularity as well as in depth to model the hierarchical relations in the data. However, the training of the GHSOM requires multiple passes over an input dataset. This makes it difficult to use the GHSOM for massive datasets. In this thesis work, we propose a Map-Reduce variant of the GHSOM called MR-GHSOM, which is capable of processing massive datasets. The MR-GHSOM is implemented using the Apache Spark cluster computing engine and leverages the popular Map-Reduce programming model. This enables us to exploit the usefulness and dynamic capabilities of the GHSOM even for a large dataset. Moreover, the conventional GHSOM algorithm can handle datasets with numeric attributes only. This is owing to the fact that it relies heavily on the Euclidean space dissimilarity measures of the attribute vectors. The MR-GHSOM further extends the GHSOM to handle mixed attribute - numeric and categorical - datasets. It accomplishes this by adopting the distance hierarchy approach of managing mixed attribute datasets. The proposed MR-GHSOM is thus capable of handling massive datasets containing mixed attributes. To demonstrate the effectiveness of the MR-GHSOM in terms of clustering of mixed attribute datasets, we present the results produced by the MR-GHSOM on some popular datasets. We further train our MR-GHSOM on a Census dataset containing mixed attributes and provide an analysis of the results.

We use ERA-Interim reanalysis data of 2 meter temperature to perform a pattern analysis of the Arctic temperatures exploiting an artificial neural network called Self Organizing-Map (SOM). The SOM method is used as a cluster analysis tool where the number of clusters has to be specified by the user. The different sized SOMs are analyzed in terms of how the size changes the representation of specific features. The results confirm that the larger the SOM is chosen the larger will be the root mean square error (RMSE) for the given SOM, which is followed by the fact that a larger number of patterns can reproduce more specific features for the temperature. / Wir benutzten das künstliche neuronale Netzwerk Self Organizing-Map (SOM), um eine Musteranalyse von ERA-Interim Reanalysedaten durchzuführen. Es wurden SOMs mit verschiedener Musteranzahl verglichen. Die Ergebnisse zeigen, dass SOMs mit einer größeren Musteranzahl deutlich spezifischere Muster produzieren im Vergleich zu SOMs mit geringen Musteranzahlen. Dies zeigt sich unter anderem in der Betrachtung der mittleren quadratischen Abweichung (RMSE) der Muster zu den zugeordneten ERA Daten.

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What organized crime is and how it can be prevented are two of the key questions in both organized crime research and criminal policy. However, despite many attempts, organized crime research, the criminal justice system and criminal policy have failed to provide a shared and recognized conceptual definition of organized crime, which has opened the door to political interpretations. Organized crime is presented as an objective reality—mostly based on anecdotal empirical evidence and generic descriptions—and has been understood, as being intrinsically different from social organization, and this has been a justification for treating organized crime conceptually separately. In this dissertation, the concept of organized crime is deconstructed and analyzed. Based on five studies and an introductory chapter, I argue that organized crime is an overarching concept based on an abstraction of different underlying concepts, such as gang, mafia, and network, which are in turn semi-overarching and overlapping abstractions of different crime phenomena, such as syndicates, street-gangs, and drug networks. This combination of a generic concept based on underlying concepts, which are themselves subject to similar conceptual difficulties, has given rise to a conceptual confusion surrounding the term and the concept of organized crime. The consequences of this conceptual confusion are not only an issue of semantics, but have implications for our understanding of the nature of criminal collaboration as well as both legal and policy consequences. By combining different observers, methods and empirical materials relating to dimensions of criminal collaboration, I illustrate the strong analogies that exist between forms of criminal collaboration and the theory of social organization. I argue in this dissertation that criminal organizing is not intrinsically different from social organizing. In fact, the dissertation illustrates the existence of strong analogies between patterns of criminal organizing and the elements of social organizations. But depending on time and context, some actions and forms of organizing are defined as criminal, and are then, intentionally or unintentionally, presumed to be intrinsically different from social organizing. Since the basis of my argument is that criminal organizing is not intrinsically different from social organizing, I advocate that the study of organized crime needs to return to the basic principles of social organization in order to understand the emergence of, and the underlying mechanism that gives rise to, the forms of criminal collaboration that we seek to explain. To this end, a new general analytical framework, “criminal organizing”, that brings the different forms of criminal organizations and their dimensions together under a single analytical tool, is proposed as an example of how organizational sociology can advance organized crime research and clarify the chaotic concept of organized crime. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Manuscript.</p><p> </p>

An individual's initial acceptance of a recruitment pitch from a community-based social movement organization is usually based upon minimal information about the group and its efforts. It is only during the subsequent period of orientation that new members begin to learn more about the organization. During this period, the retention of new members is dependent on the successful alignment of individual and organizational frames. The failure to achieve such an alignment is likely to result in the new member's departure from the organization. This study explores the frame alignment process during early orientation to community-based SMOs. Using nineteen qualitative interviews with three different community organizing efforts in Baltimore, the study suggests that organizational members feel most motivated to continue involvement when they feel that the organization is effective.

Approved for public release; distribution is unlimited / The Venezuelan Navy, depends on its personnel, equipment, and facilities to successfully accomplish the Navy's mission. Therefore Naval operations rely on the ability of the officers to plan, organize, lead, and control the organization. Naval officers, whether senior or junior, hold positions as managers. For this reason a study was made of the educational background of officer candidates to determine the amount of management education they have received at the Naval Academy. From this study it was learned that although naval officers are generally well educated and trained; they are weak in the management area. In this thesis an attempt is made to show the basic elements necessary to improve such managerial education. Conclusions were drawn and recommendations were made to help the managerial development of Venezuela's Naval officers. / http://archive.org/details/improvementofman00camp / Commander, Venezuelan Navy

by John Pui-fai Sum. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 99-104). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Idea of SSOM --- p.3 / Chapter 1.3 --- Other Approaches --- p.3 / Chapter 1.4 --- Contribution of the Thesis --- p.4 / Chapter 1.5 --- Outline of Thesis --- p.5 / Chapter 2 --- Self-Organizing Map --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Algorithm of SOM --- p.8 / Chapter 2.3 --- Illustrative Example --- p.10 / Chapter 2.4 --- Property of SOM --- p.14 / Chapter 2.4.1 --- Convergence property --- p.14 / Chapter 2.4.2 --- Topological Order --- p.15 / Chapter 2.4.3 --- Objective Function of SOM --- p.15 / Chapter 2.5 --- Conclusion --- p.17 / Chapter 3 --- Algorithms for Soft Self-Organizing Map --- p.18 / Chapter 3.1 --- Competitive Learning and Soft Competitive Learning --- p.19 / Chapter 3.2 --- How does SOM generate ordered map? --- p.21 / Chapter 3.3 --- Algorithms of Soft SOM --- p.23 / Chapter 3.4 --- Simulation Results --- p.25 / Chapter 3.4.1 --- One dimensional map under uniform distribution --- p.25 / Chapter 3.4.2 --- One dimensional map under Gaussian distribution --- p.27 / Chapter 3.4.3 --- Two dimensional map in a unit square --- p.28 / Chapter 3.5 --- Conclusion --- p.30 / Chapter 4 --- Application to Uncover Vowel Relationship --- p.31 / Chapter 4.1 --- Experiment Set Up --- p.32 / Chapter 4.1.1 --- Network structure --- p.32 / Chapter 4.1.2 --- Training procedure --- p.32 / Chapter 4.1.3 --- Relationship Construction Scheme --- p.34 / Chapter 4.2 --- Results --- p.34 / Chapter 4.2.1 --- Hidden-unit labeling for SSOM2 --- p.34 / Chapter 4.2.2 --- Hidden-unit labeling for SOM --- p.35 / Chapter 4.3 --- Conclusion --- p.37 / Chapter 5 --- Application to vowel data transmission --- p.42 / Chapter 5.1 --- Introduction --- p.42 / Chapter 5.2 --- Simulation --- p.45 / Chapter 5.2.1 --- Setup --- p.45 / Chapter 5.2.2 --- Noise model and demodulation scheme --- p.46 / Chapter 5.2.3 --- Performance index --- p.46 / Chapter 5.2.4 --- Control experiment: random coding scheme --- p.46 / Chapter 5.3 --- Results --- p.47 / Chapter 5.3.1 --- Null channel noise (σ = 0) --- p.47 / Chapter 5.3.2 --- Small channel noise (0 ≤ σ ≤1) --- p.49 / Chapter 5.3.3 --- Large channel noise (1 ≤σ ≤7) --- p.49 / Chapter 5.3.4 --- Very large channel noise (σ > 7) --- p.49 / Chapter 5.4 --- Conclusion --- p.50 / Chapter 6 --- Convergence Analysis --- p.53 / Chapter 6.1 --- Kushner and Clark Lemma --- p.53 / Chapter 6.2 --- Condition for the Convergence of Jou's Algorithm --- p.54 / Chapter 6.3 --- Alternative Proof on the Convergence of Competitive Learning --- p.56 / Chapter 6.4 --- Convergence of Soft SOM --- p.58 / Chapter 6.5 --- Convergence of SOM --- p.60 / Chapter 7 --- Conclusion --- p.61 / Chapter 7.1 --- Limitations of SSOM --- p.62 / Chapter 7.2 --- Further Research --- p.63 / Chapter A --- Proof of Corollary1 --- p.65 / Chapter A.l --- Mean Average Update --- p.66 / Chapter A.2 --- Case 1: Uniform Distribution --- p.68 / Chapter A.3 --- Case 2: Logconcave Distribution --- p.70 / Chapter A.4 --- Case 3: Loglinear Distribution --- p.72 / Chapter B --- Different Senses of neighborhood --- p.79 / Chapter B.l --- Static neighborhood: Kohonen's sense --- p.79 / Chapter B.2 --- Dynamic neighborhood --- p.80 / Chapter B.2.1 --- Mou-Yeung Definition --- p.80 / Chapter B.2.2 --- Martinetz et al. Definition --- p.81 / Chapter B.2.3 --- Tsao-Bezdek-Pal Definition --- p.81 / Chapter B.3 --- Example --- p.82 / Chapter B.4 --- Discussion --- p.84 / Chapter C --- Supplementary to Chapter4 --- p.86 / Chapter D --- Quadrature Amplitude Modulation --- p.92 / Chapter D.l --- Amplitude Modulation --- p.92 / Chapter D.2 --- QAM --- p.93 / Bibliography --- p.99