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This manual describes artificial binary data scenarios. These data sets can be used to compare the performance of algorithms for market segmentation. The data sets described in this manual are available as packages for R (Splus) and as ASCII-files under htttp://www.ci.tuwien.ac.at/SFB/. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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During times of stock market turbulence and crises, monitoring the clustering behaviour of financial instruments allows one to better understand the behaviour of the stock market and the associated systemic risks. In the study undertaken, I apply an effective and performant approach to classify data clusters in order to better understand correlations between stocks. The novel methods aim to address the lack of effective algorithms to deal with high-performance cluster analysis in the context of large complex real-time low-latency data-sets. I apply an efficient and novel data clustering approach, namely the Giada and Marsili log-likelihood function derived from the Noh model and use a Parallel Genetic Algorithm in order to isolate residual data clusters. Genetic Algorithms (GAs) are a very versatile methodology for scientific computing, while the application of Parallel Genetic Algorithms (PGAs) further increases the computational efficiency. They are an effective vehicle to mine data sets for information and traits. However, the traditional parallel computing environment can be expensive. I focused on adopting NVIDIAs Compute Unified Device Architecture (CUDA) programming model in order to develop a PGA framework for my computation solution, where I aim to efficiently filter out residual clusters. The results show that the application of the PGA with the novel clustering function on the CUDA platform is quite effective to improve the computational efficiency of parallel data cluster analysis.

by Lee Wing Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 81-90). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / List of Figures --- p.vii / List of Tables --- p.viii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Clustering --- p.1 / Chapter 1.1.1 --- Hierarchical Classification --- p.2 / Chapter 1.1.2 --- Partitional Classification --- p.3 / Chapter 1.1.3 --- Comparative Analysis --- p.4 / Chapter 1.2 --- Cluster Analysis and Traveling Salesman Problem --- p.5 / Chapter 1.3 --- Solving Clustering Problem --- p.7 / Chapter 1.4 --- Genetic Algorithms --- p.9 / Chapter 1.5 --- Outline of Work --- p.11 / Chapter 2 --- The Clustering Algorithms and Applications --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Traveling Salesman Problem --- p.14 / Chapter 2.2.1 --- Related Work on TSP --- p.14 / Chapter 2.2.2 --- Solving TSP using Genetic Algorithm --- p.15 / Chapter 2.3 --- Applications --- p.22 / Chapter 2.3.1 --- Clustering for Vertical Partitioning Design --- p.22 / Chapter 2.3.2 --- Horizontal Partitioning a Relational Database --- p.36 / Chapter 2.3.3 --- Object-Oriented Database Design --- p.42 / Chapter 2.3.4 --- Document Database Design --- p.49 / Chapter 2.4 --- Conclusions --- p.53 / Chapter 3 --- The Experiments for Vertical Partitioning Problem --- p.55 / Chapter 3.1 --- Introduction --- p.55 / Chapter 3.2 --- Comparative Study --- p.56 / Chapter 3.3 --- Experimental Results --- p.59 / Chapter 3.4 --- Conclusions --- p.61 / Chapter 4 --- Three New Operators for TSP --- p.62 / Chapter 4.1 --- Introduction --- p.62 / Chapter 4.2 --- Enhanced Cost Edge Recombination Operator --- p.63 / Chapter 4.3 --- Shortest Path Operator --- p.66 / Chapter 4.4 --- Shortest Edge Operator --- p.69 / Chapter 4.5 --- The Experiments --- p.71 / Chapter 4.5.1 --- Experimental Results for a 48-city TSP --- p.71 / Chapter 4.5.2 --- Experimental Results for Problems in TSPLIB --- p.73 / Chapter 4.6 --- Conclusions --- p.77 / Chapter 5 --- Conclusions --- p.78 / Chapter 5.1 --- Summary of Achievements --- p.78 / Chapter 5.2 --- Future Development --- p.80 / Bibliography --- p.81

Ng Ka Ka. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 114-120). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.vii / Chapter I --- Projective Clustering --- p.1 / Chapter 1 --- Introduction to Projective Clustering --- p.2 / Chapter 2 --- Related Work to Projective Clustering --- p.7 / Chapter 2.1 --- CLARANS - Graph Abstraction and Bounded Optimization --- p.8 / Chapter 2.1.1 --- Graph Abstraction --- p.8 / Chapter 2.1.2 --- Bounded Optimized Random Search --- p.9 / Chapter 2.2 --- OptiGrid ´ؤ Grid Partitioning Approach and Density Estimation Function --- p.9 / Chapter 2.2.1 --- Empty Space Phenomenon --- p.10 / Chapter 2.2.2 --- Density Estimation Function --- p.11 / Chapter 2.2.3 --- Upper Bound Property --- p.12 / Chapter 2.3 --- CLIQUE and ENCLUS - Subspace Clustering --- p.13 / Chapter 2.3.1 --- Monotonicity Property of Subspaces --- p.14 / Chapter 2.4 --- PROCLUS Projective Clustering --- p.15 / Chapter 2.5 --- ORCLUS - Generalized Projective Clustering --- p.16 / Chapter 2.5.1 --- Singular Value Decomposition SVD --- p.17 / Chapter 2.6 --- "An ""Optimal"" Projective Clustering" --- p.17 / Chapter 3 --- EPC : Efficient Projective Clustering --- p.19 / Chapter 3.1 --- Motivation --- p.19 / Chapter 3.2 --- Notations and Definitions --- p.21 / Chapter 3.2.1 --- Density Estimation Function --- p.22 / Chapter 3.2.2 --- 1-d Histogram --- p.23 / Chapter 3.2.3 --- 1-d Dense Region --- p.25 / Chapter 3.2.4 --- Signature Q --- p.26 / Chapter 3.3 --- The overall framework --- p.28 / Chapter 3.4 --- Major Steps --- p.30 / Chapter 3.4.1 --- Histogram Generation --- p.30 / Chapter 3.4.2 --- Adaptive discovery of dense regions --- p.31 / Chapter 3.4.3 --- Count the occurrences of signatures --- p.36 / Chapter 3.4.4 --- Find the most frequent signatures --- p.36 / Chapter 3.4.5 --- Refine the top 3m signatures --- p.37 / Chapter 3.5 --- Time and Space Complexity --- p.38 / Chapter 4 --- EPCH: An extension and generalization of EPC --- p.40 / Chapter 4.1 --- Motivation of the extension --- p.40 / Chapter 4.2 --- Distinguish clusters by their projections in different subspaces --- p.43 / Chapter 4.3 --- EPCH: a generalization of EPC by building histogram with higher dimensionality --- p.46 / Chapter 4.3.1 --- Multidimensional histograms construction and dense re- gions detection --- p.46 / Chapter 4.3.2 --- Compressing data objects to signatures --- p.47 / Chapter 4.3.3 --- Merging Similar Signature Entries --- p.49 / Chapter 4.3.4 --- Associating membership degree --- p.51 / Chapter 4.3.5 --- The choice of Dimensionality d of the Histogram --- p.52 / Chapter 4.4 --- Implementation of EPC2 --- p.53 / Chapter 4.5 --- Time and Space Complexity of EPCH --- p.54 / Chapter 5 --- Experimental Results --- p.56 / Chapter 5.1 --- Clustering Quality Measurement --- p.56 / Chapter 5.2 --- Synthetic Data Generation --- p.58 / Chapter 5.3 --- Experimental setup --- p.59 / Chapter 5.4 --- Comparison between EPC and PROCULS --- p.60 / Chapter 5.5 --- Comparison between EPCH and ORCLUS --- p.62 / Chapter 5.5.1 --- Dimensionality of the original space and the associated subspace --- p.65 / Chapter 5.5.2 --- Projection not parallel to original axes --- p.66 / Chapter 5.5.3 --- Data objects belong to more than one cluster under fuzzy clustering --- p.67 / Chapter 5.6 --- Scalability of EPC --- p.68 / Chapter 5.7 --- Scalability of EPC2 --- p.69 / Chapter 6 --- Conclusion --- p.71 / Chapter II --- Multiple Tables Association Rules Mining --- p.74 / Chapter 7 --- Introduction to Multiple Tables Association Rule Mining --- p.75 / Chapter 7.1 --- Problem Statement --- p.77 / Chapter 8 --- Related Work to Multiple Tables Association Rules Mining --- p.80 / Chapter 8.1 --- Aprori - A Bottom-up approach to generate candidate sets --- p.80 / Chapter 8.2 --- VIPER - Vertical Mining with various optimization techniques --- p.81 / Chapter 8.2.1 --- Vertical TID Representation and Mining --- p.82 / Chapter 8.2.2 --- FORC --- p.83 / Chapter 8.3 --- Frequent Itemset Counting across Multiple Tables --- p.84 / Chapter 9 --- The Proposed Method --- p.85 / Chapter 9.1 --- Notations --- p.85 / Chapter 9.2 --- Converting Dimension Tables to internal representation --- p.87 / Chapter 9.3 --- The idea of discovering frequent itemsets without joining --- p.89 / Chapter 9.4 --- Overall Steps --- p.91 / Chapter 9.5 --- Binding multiple Dimension Tables --- p.92 / Chapter 9.6 --- Prefix Tree for FT --- p.94 / Chapter 9.7 --- Maintaining frequent itemsets in FI-trees --- p.96 / Chapter 9.8 --- Frequency Counting --- p.99 / Chapter 10 --- Experiments --- p.102 / Chapter 10.1 --- Synthetic Data Generation --- p.102 / Chapter 10.2 --- Experimental Findings --- p.106 / Chapter 11 --- Conclusion and Future Works --- p.112 / Bibliography --- p.114

Essa dissertação tem como objetivo analisar como ocorre o desenvolvimento de recursos do cluster para a internacionalização em diferentes estágios do ciclo de vida do cluster. Sendo assim, buscou-se compreender a dinâmica envolvida entre o estágio do ciclo de vida do cluster e os recursos para a internacionalização. Para isso, realizou-se uma pesquisa qualitativa através do método de estudo de caso, realizado em dois clusters da indústria calçadista já consolidados. O cluster de Fermo e Macerata (Itália), que é uma aglomeração calçadista estabelecida no pós-guerra através de políticas italianas de desenvolvimento regional; e o cluster do Vale dos Sinos (Brasil), que se estabeleceu após a chegada dos imigrantes alemães na região, contudo, apenas adquiriu níveis mundiais de qualidade produtiva quando se tornou terceirizado dos EUA. O primeiro se encontra em estágio de adaptação e o último se encontra atualmente em estágio de declínio. Inicialmente, buscou-se a descrição do contexto mundial da indústria calçadista, bem como sua trajetória história. Posteriormente, realizou-se a análise, que levantou dados da trajetória dos clusters e da localidade, desde a emergência dos clusters até o momento atual. Em seguida, os casos foram analisados a partir das dimensões de análise: trajetória de internacionalização do cluster; ciclo de vida do cluster; e recursos para internacionalização em diferentes dos estágios. Por fim, compreendeu-se que o contexto da indústria interfere diretamente no Ciclo de Vida do Cluster nos casos investigados e que os atores institucionais possuem relevante papel na articulação e renovação dos recursos da aglomeração, bem como em seu direcionamento estratégico internacional. Assim, a Resource-Based View (RBV) contribui para o entendimento da trajetória e do atual estágio do Ciclo de Vida do Cluster, contudo, não há como definir um modelo único para o entendimento da trajetória uma vez que cada cluster é único. Constatou-se que há relação entre os recursos do cluster e o estágio do ciclo de vida no qual o cluster se encontra. Contudo, percebeu-se que ao observar o cluster sob a ótica da RBV, a governança dos atores assume um papel crítico na articulação e renovação dos recursos competitivos coletivos, abrindo a perspectiva do cluster para novas oportunidades em um contexto global cuja localidade mantém sua relevância. Evidenciou-se que, apesar de ambos os clusters possuírem alto percentual de firmas internacionalizadas, não há internacionalização em nível de cluster. Abrir-se para a uma estratégia de internacionalização em nível de cluster poderá abrir novas possibilidades para as empresas e para a competitividade da região a qual o cluster está inserido. / This dissertation aims to analyse the development of cluster resources for internationalization at different stages of cluster life cycle. Therefore, it was sought to understand the dynamics involved between the stage of the cluster life cycle and its resources for internationalization. For this, a qualitative research using the case study method was carried out in two already consolidated clusters of the footwear industry. The cluster of Fermo and Macerata (Italy), is a footwear cluster established in the post-war period through Italian regional development policies; and the Sinos Valley cluster (Brazil), which was established after the arrival of German immigrants in the region and yet only acquired world levels of productive quality when it became outsourced in the USA. The first cluster is already in the transformation stage and the latter is currently in a declining stage. Initially, it was sought to describe the global context of the footwear industry as well as its historic trajectory. Subsequently, the analysis was performed, which collected data on the trajectory of clusters and its locality, from the cluster emergence until the present moment. Then, the cases were analysed from the three dimensions of analysis: cluster internationalization trajectory; cluster life cycle; and resources for internationalization in different stages. Finally, it was understood that the cluster life cycle is directly affected by the industry context in the investigated cases and that institutional actors have a relevant role in the articulation and renewal of agglomeration resources, as well as in their international strategic directions. Thereby, the Resource-Based View contributes to the understanding of the trajectory and the current stage of Cluster Life Cycle, however, there is no way to define a single model for the understanding of the trajectory since each cluster is unique. It was verified that there is a relation between the resources of the cluster and the stage in which the cluster is in its life cycle. Nevertheless, it was observed that when looking at the cluster from a Resource-Based View perspective, cluster management plays a critical role in articulating and renewing collective competitive resources, opening to the cluster perspective new opportunities in a global context whose locality maintains its relevance. It became clear that, although both cluster have a very high percentage of internationalized companies, there is no internationalization at the cluster level. Opening to an internationalization strategy at the cluster level may create new possibilities for companies and for the region to which the cluster is inserted.

Lau Hei Yuet. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 74-76). / Abstracts in English and Chinese. / Abstract --- p.i / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Cluster Analysis --- p.1 / Chapter 1.2 --- Dissimilarity Measures --- p.3 / Chapter 1.2.1 --- Continuous Data --- p.4 / Chapter 1.2.2 --- Categorical and Nominal Data --- p.4 / Chapter 1.2.3 --- Mixed Data --- p.5 / Chapter 1.2.4 --- Missing Data --- p.6 / Chapter 1.3 --- Outline of the thesis --- p.6 / Chapter 2 --- Clustering Algorithms --- p.9 / Chapter 2.1 --- The k-means Algorithm Family --- p.9 / Chapter 2.1.1 --- The Algorithms --- p.9 / Chapter 2.1.2 --- Choosing the Number of Clusters - the MaxMin Algo- rithm --- p.12 / Chapter 2.1.3 --- Starting Configuration - the MaxMin Algorithm --- p.16 / Chapter 2.2 --- Clustering Using Unidimensional Scaling --- p.16 / Chapter 2.2.1 --- Unidimensional Scaling --- p.16 / Chapter 2.2.2 --- Procedures --- p.17 / Chapter 2.2.3 --- Guttman's Updating Algorithm --- p.18 / Chapter 2.2.4 --- Pliner's Smoothing Algorithm --- p.18 / Chapter 2.2.5 --- Starting Configuration --- p.19 / Chapter 2.2.6 --- Choosing the Number of Clusters --- p.21 / Chapter 2.3 --- Cluster Validation --- p.23 / Chapter 2.3.1 --- Continuous Data --- p.23 / Chapter 2.3.2 --- Nominal Data --- p.24 / Chapter 2.3.3 --- Resampling Method --- p.25 / Chapter 2.4 --- Conclusion --- p.27 / Chapter 3 --- Experimental Results --- p.29 / Chapter 3.1 --- Simulated Data 1 --- p.29 / Chapter 3.2 --- Simulated Data 2 --- p.35 / Chapter 3.3 --- Iris Data --- p.41 / Chapter 3.4 --- Wine Data --- p.47 / Chapter 3.5 --- Mushroom Data --- p.53 / Chapter 3.6 --- Conclusion --- p.59 / Chapter 4 --- Large Database --- p.61 / Chapter 4.1 --- Sliding Windows Algorithm --- p.61 / Chapter 4.2 --- Two-stage Algorithm --- p.63 / Chapter 4.3 --- Three-stage Algorithm --- p.65 / Chapter 4.4 --- Experimental Results --- p.66 / Chapter 4.5 --- Conclusion --- p.68 / Chapter A --- Algorithms --- p.69 / Chapter A.1 --- MaxMin Algorithm --- p.69 / Chapter A.2 --- Sliding Windows Algorithm --- p.70 / Chapter A.3 --- Two-stage Algorithm - Stage One --- p.72 / Chapter A.4 --- Two-stage Algorithm - Stage Two --- p.73 / Bibliography --- p.74

Finally, we study how to construct an appropriate graph for spectral clustering. Given a local similarity matrix (a graph), we propose an iterative regularization procedure to iteratively enhance its cluster structure, leading to a global similarity matrix. Significant improvement of clustering performance is observed when the new graph is used for spectral clustering. / In this thesis, we consider the general problem of classifying a data set into a number of subsets, which has been one of the most fundamental problems in machine learning. Specifically, we mainly address the following four common learning problems in three active research fields: semi-supervised classification, semi-supervised clustering, and unsupervised clustering. The first problem we consider is semi-supervised classification from both unlabeled data and pairwise constraints. The pairwise constraints specify which two objects belong to the same class or not. Our aim is to propagate the pairwise constraints to the entire data set. We formulate the propagation model as a semidefinite programming (SDP) problem, which can be globally solved reliably. Our approach is applicable to multi-class problems and handles class labels, pairwise constraints, or a mixture of them in a unified framework. / The second problem is semi-supervised clustering with pairwise constraints. We present a principled framework for learning a data-driven and constraint-consistent nonlinear mapping to reshape the data in a feature space. We formulate the problem as a small-scale SDP problem, whose size is independent of the numbers of the objects and the constraints. Thus it can be globally solved efficiently. Our framework has several attractive features. First, it can effectively propagate pairwise constraints, when available, to the entire data set. Second, it scales well to large-scale problems. Third, it can effectively handle noisy constraints. Fourth, in the absence of constraints, it becomes a novel kernel-based clustering algorithm that can discover linearly non-separable clusters. / Third, we deal with noise robust clustering. Many clustering algorithms, including spectral clustering, often fail on noisy data. We propose a data warping model to map the data into a new space. During the warping, each object spreads its spatial information smoothly over the data graph to other objects. After the warping, hopefully each cluster becomes compact and different clusters become well-separated, including the noise cluster that is formed by the noise objects. The proposed clustering algorithm can handle significantly noisy data, and can find the number of clusters automatically. / Li, Zhenguo. / Adviser: Liu Jianzhuang. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3604. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 121-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.