Using free data mining software and clustering algorithms to find predictors from student qualifications

In this project a research both in finding predictors via clustering techniques and in reviewing the Data Mining free software is achieved. The research is based in a case of study, from where additionally to the KDD free software used by the scientific community; a new free tool for pre-processing the data is presented. The predictors are intended for the e-learning domain as the data from where these predictors have to be inferred are student qualifications from different e-learning environments. Through our case of study not only clustering algorithms are tested but also additional goals are proposed.

Identificació de paisatges funcionals mitjançant anàlisi cluster

El terme paisatge i les seves aplicacions són cada dia més utilitzats per les administracions i altres entitats com a eina de gestió del territori. Aprofitant la gran quantitat de dades en bases compatibles amb SIG (Sistemes d’Informació Geogràfica) existents a Catalunya s’ha desenvolupat una síntesi cartogràfica on s’identifiquen els Paisatges Funcionals (PF) de Catalunya, concepte que fa referència al comportament fisico-ecològic del terreny a partir de variables topogràfiques i climàtiques convenientment transformades i agregades. S’ha utilitzat un mètode semiautomàtic i iteratiu de classificació no supervisada (clustering) que permet la creació d’una llegenda jeràrquica o nivells de generalització. S’ha obtingut com a resultat el Mapa de Paisatges Funcionals de Catalunya (MPFC) amb una llegenda de 26 categories de paisatges i 5 nivells de generalització amb una resolució espacial de 180 m. Paral·lelament, s’han realitzat validacions indirectes sobre el mapa obtingut a partir dels coneixements naturalistes i la cartografia existent, així com també d’un mapa d’incertesa (aplicant lògica difusa) que aporten informació de la fiabilitat de la classificació realitzada. Els Paisatges Funcionals obtinguts permeten relacionar zones de condicions topo-climàtiques homogènies i dividir el territori en zones caracteritzades ambientalment i no políticament amb la intenció que sigui d’utilitat a l’hora de millorar la gestió dels recursos naturals i la planificació d’actuacions humanes.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Clustering of risk behaviors with cigarette consumption: a population-based survey.

OBJECTIVE: This study assessed clustering of multiple risk behaviors (i.e., low leisure-time physical activity, low fruits/vegetables intake, and high alcohol consumption) with level of cigarette consumption. METHODS: Data from the 2002 Swiss Health Survey, a population-based cross-sectional telephone survey assessing health and self-reported risk behaviors, were used. 18,005 subjects (8052 men and 9953 women) aged 25 years old or more participated. RESULTS: Smokers more frequently had low leisure time physical activity, low fruits/vegetables intake, and high alcohol consumption than non- and ex-smokers. Frequency of each risk behavior increased steadily with cigarette consumption. Clustering of risk behaviors increased with cigarette consumption in both men and women. For men, the odds ratios of multiple (> or =2) risk behaviors other than smoking, adjusted for age, nationality, and educational level, were 1.14 (95% confidence interval: 0.97, 1.33) for ex-smokers, 1.24 (0.93, 1.64) for light smokers (1-9 cigarettes/day), 1.72 (1.36, 2.17) for moderate smokers (10-19 cigarettes/day), and 3.07 (2.59, 3.64) for heavy smokers (> or =20 cigarettes/day) versus non-smokers. Similar odds ratios were found for women for corresponding groups, i.e., 1.01 (0.86, 1.19), 1.26 (1.00, 1.58), 1.62 (1.33, 1.98), and 2.75 (2.30, 3.29). CONCLUSIONS: Counseling and intervention with smokers should take into account the strong clustering of risk behaviors with level of cigarette consumption.

Clustering compositional data trajectories

Our essay aims at studying suitable statistical methods for the clustering ofcompositional data in situations where observations are constituted by trajectories ofcompositional data, that is, by sequences of composition measurements along a domain.Observed trajectories are known as “functional data” and several methods have beenproposed for their analysis.In particular, methods for clustering functional data, known as Functional ClusterAnalysis (FCA), have been applied by practitioners and scientists in many fields. To ourknowledge, FCA techniques have not been extended to cope with the problem ofclustering compositional data trajectories. In order to extend FCA techniques to theanalysis of compositional data, FCA clustering techniques have to be adapted by using asuitable compositional algebra.The present work centres on the following question: given a sample of compositionaldata trajectories, how can we formulate a segmentation procedure giving homogeneousclasses? To address this problem we follow the steps described below.First of all we adapt the well-known spline smoothing techniques in order to cope withthe smoothing of compositional data trajectories. In fact, an observed curve can bethought of as the sum of a smooth part plus some noise due to measurement errors.Spline smoothing techniques are used to isolate the smooth part of the trajectory:clustering algorithms are then applied to these smooth curves.The second step consists in building suitable metrics for measuring the dissimilaritybetween trajectories: we propose a metric that accounts for difference in both shape andlevel, and a metric accounting for differences in shape only.A simulation study is performed in order to evaluate the proposed methodologies, usingboth hierarchical and partitional clustering algorithm. The quality of the obtained resultsis assessed by means of several indices

Ciclo de vida organizacional pautado no modelo de Lester, Parnell e Carraher (2003) e na lógica fuzzy: classificação de empresas de um segmento industrial de Santa Catarina

Neste estudo, objetiva-se identificar os estágios do ciclo de vida organizacional pautados no modelo de Lester, Parnell e Carraher (2003) das empresas do segmento industrial de máquinas, aparelhos e materiais elétricos do estado de Santa Catarina. Pesquisa descritiva, com abordagem quantitativa, foi realizada por meio de levantamento com aplicação de questionário aos gestores das empresas. A população constituiu-se das 264 empresas desse segmento econômico, listadas na Secretaria da Fazenda do Estado de Santa Catarina, e a amostra não aleatória das 40 empresas que responderam a pesquisa. As variáveis de identificação dos estágios de ciclo de vida utilizadas no questionário foram extraídas de Lester, Parnell e Carraher (2003). Os dados da pesquisa foram submetidos à técnica estatística denominada lógica fuzzy. Os resultados da pesquisa demonstraram que 57,5% das empresas foram classificadas no estágio do nascimento, 15% do, 7,5% da Maturidade, 10% do rejuvenescimento e 10% do declínio. Concluiu-se que determinados estágios do ciclo de vida organizacional estão próximos uns dos outros e que não se pode perceber claramente uma progressão determinista nas fases do ciclo de vida, como uma sequência única, definitiva e irreversível, no sentido tradicional biológico.

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Determinants of acquisition completion: a relational perspective

The strategic literature on relatedness in the context of mergers and acquisitions (M&As) is extensive, yet we know little about whether or how relatedness has an influence on the announcement to completion stage of the M&A process. Drawing on research on intra-industry competition and relational capabilities, we seek to shed light on the relatedness debate by examining the strategic forces that affect the completion of an announced related M&A, accounting for financial and organizational factors. We also explore additional strategic forces that might amplify or attenuate the negative effect of relatedness on deal completion. We test and find support for our hypotheses using longitudinal data from a sample of the largest M&A announcements in the world from 1991 to 2001.

Debt enforcement and relational contracting

We examine how third-party debt enforcement affects the emergence and performance ofrelational contracts in credit markets. We implement an experiment with finitely repeatedcredit relationships in which borrowers can default. In the weak enforcement treatmentdefaulting borrowers can keep their funds invested. In the strong enforcement treatmentdefaulting borrowers have to liquidate their investment. Under weak enforcement fewerrelationships emerge in which loans are extended and repaid. When such relationships doemerge they exhibit a lower credit volume than under strong enforcement. These findingssuggest that relational contracting in credit markets requires a minimum standard of thirdpartydebt enforcement.

Effective atomic orbitals for fuzzy atoms

The method of extracting effective atomic orbitals and effective minimal basis sets from molecular wave function characterizing the state of an atom in a molecule is developed in the framework of the "fuzzy" atoms. In all cases studied, there were as many effective orbitals that have considerable occupation numbers as orbitals in the classical minimal basis. That is considered to be of high conceptual importance

Energy partitioning for "fuzzy" atoms

The total energy of molecule in terms of 'fuzzy atoms' presented as sum of one- and two-atomic energy components is described. The divisions of three-dimensional physical space into atomic regions exhibit continuous transition from one to another. The energy components are on chemical energy scale according to proper definitions. The Becke's integration scheme and weight function determines realization of method which permits effective numerical integrations

Conserved chromosomal clustering of genes governed by chromatin regulators in Drosophila

BACKGROUND: The trithorax group (trxG) and Polycomb group (PcG) proteins are responsible for the maintenance of stable transcriptional patterns of many developmental regulators. They bind to specific regions of DNA and direct the post-translational modifications of histones, playing a role in the dynamics of chromatin structure. RESULTS: We have performed genome-wide expression studies of trx and ash2 mutants in Drosophila melanogaster. Using computational analysis of our microarray data, we have identified 25 clusters of genes potentially regulated by TRX. Most of these clusters consist of genes that encode structural proteins involved in cuticle formation. This organization appears to be a distinctive feature of the regulatory networks of TRX and other chromatin regulators, since we have observed the same arrangement in clusters after experiments performed with ASH2, as well as in experiments performed by others with NURF, dMyc, and ASH1. We have also found many of these clusters to be significantly conserved in D. simulans, D. yakuba, D. pseudoobscura and partially in Anopheles gambiae. CONCLUSION: The analysis of genes governed by chromatin regulators has led to the identification of clusters of functionally related genes conserved in other insect species, suggesting this chromosomal organization is biologically important. Moreover, our results indicate that TRX and other chromatin regulators may act globally on chromatin domains that contain transcriptionally co-regulated genes.

Using qualia information to identify lexical semantic classes in an unsupervised clustering task

Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse data and noise in our task.