Efficient mining of association rules based on formal concept analysis

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, TITANIC, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of the systems. We consider their underlying data structures – socalled folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures – so-called folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag cooccurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

Lost in Research - Wissensmanagement in komplexen (psychoanalytischen) Forschungsprojekten

Die Wissenschaft weist im Zuge der Entwicklung von der Industrie- zu einer Wissensgesellschaft einschneidende Veränderungen in der Wissensordnung auf, welche sich bis hin zu einem zunehmenden Verlust der wissenschaftlichen Selbststeuerungsmechanismen bemerkbar machen und einen veränderten Umgang mit dem generierten Wissensschatz erfordern. Nicht nur Änderungen in der Wissensordnung und -produktion stellen die Psychoanalyse vor neue Herausforderungen: In den letzten Jahrzehnten geriet sie als Wissenschaft und Behandlungsverfahren zunehmend in die Kritik und reagierte mit einer konstruktiven Diskussion um ein dem Forschungsgegenstand – die Untersuchung unbewusster Prozesse und Fantasien – adäquates psychoanalytisches Forschungsverständnis. Die Auseinandersetzung mit Forderungen gesellschaftlicher Geldgeber, politischer Vertreter und Interessensgruppen wie auch der wissenschaftlichen Community stellt die Psychoanalyse vor besondere Herausforderungen. Um wissenschaftsexternen wie -internen Gütekriterien zu genügen, ist häufig ein hoher personeller, materieller, finanzieller, methodischer wie organisatorischer Aufwand unabdingbar, wie das Beispiel des psychoanalytischen Forschungsinstitutes Sigmund-Freud-Institut zeigt. Der steigende Aufwand schlägt sich in einer zunehmenden Komplexität des Forschungsprozesses nieder, die unter anderem in den vielschichtigen Fragestellungen und Zielsetzungen, dem vermehrt interdisziplinären, vernetzten Charakter, dem Umgang mit dem umfangreichen, hochspezialisierten Wissen, der Methodenvielfalt, etc. begründet liegt. Um jener Komplexität des Forschungsprozesses gerecht zu werden, ist es zunehmend erforderlich, Wege des Wissensmanagement zu beschreiten. Tools wie z. B. Mapping-Verfahren stellen unterstützende Werkzeuge des Wissensmanagements dar, um den Herausforderungen des Forschungsprozesses zu begegnen. In der vorliegenden Arbeit werden zunächst die veränderten Forschungsbedingungen und ihre Auswirkungen auf die Komplexität des Forschungsprozesses - insbesondere auch des psychoanalytischen Forschungsprozesses - reflektiert. Die mit der wachsenden Komplexität einhergehenden Schwierigkeiten und Herausforderungen werden am Beispiel eines interdisziplinär ausgerichteten EU-Forschungsprojektes näher illustriert. Um dieser wachsenden Komplexität psychoanalytischer Forschung erfolgreich zu begegnen, wurden in verschiedenen Forschungsprojekten am Sigmund-Freud-Institut Wissensmanagement-Maßnahmen ergriffen. In der vorliegenden Arbeit wird daher in einem zweiten Teil zunächst auf theoretische Aspekte des Wissensmanagements eingegangen, die die Grundlage der eingesetzten Wissensmanagement-Instrumente bildeten. Dabei spielen insbesondere psychologische Aspekte des Wissensmanagements eine zentrale Rolle. Zudem werden die konkreten Wissensmanagement-Tools vorgestellt, die in den verschiedenen Forschungsprojekten zum Einsatz kamen, um der wachsenden Komplexität psychoanalytischer Forschung zu begegnen. Abschließend werden die Hauptthesen der vorliegenden Arbeit noch einmal reflektiert und die geschilderten Techniken des Wissensmanagements im Hinblick auf ihre Vor- und Nachteile kritisch diskutiert.

Caracterización de un modelo de gestión del conocimiento aplicable a las funciones universitarias de investigación y extensión: caso Universidad CES

Existen en la actualidad múltiples modelos de gestión de conocimiento y medición del capital humano, los cuales son aplicados en las organizaciones, pero ninguno de éstos ha sido diseñado para Instituciones de Educación Superior. En este trabajo se hace un recuento de algunos de los modelos de gestión del conocimiento y capital intelectual más destacados como el Modelo de conversión del conocimiento de Nonaka y Takeuchi, el Modelo de GC de Arthur Andersen, el Cuadro de Mando Integral de Kaplan y Norton, entre otros, pero es a partir del Modelo Organizacional Estrella de Galbraith que se presenta una propuesta teórica para caracterizar un modelo de gestión del conocimiento aplicable a las funciones universitarias de investigación y extensión en la Universidad CES – Medellín, Colombia, a través de una investigación cualitativa en donde, a partir de la correlación entre la teoría general de la GC, particularmente de los modelos y el análisis de las características de la Universidad CES, así como la revisión sistemática, el grupo focal y el análisis documental se propone el Modelo Hexagonal de GC.

Gestión tecnológica hospitalaria : Un enfoque sistémico

Este texto contribuirá a que la institución de salud se organice y prepare la información necesaria para emprender el largo y tortuoso camino de la determinación de la razón costo/beneficio y de la acreditación. Además, podrá ser muy útil para los estudiantes de los programas de pregrado y posgrado de ingeniería biomédica que se quieran especializar en la gestión de tecnologías del equipamiento biomédico y la ingeniería clínica. También podrá ser usado como guía de referencia por personas que estén directamente vinculadas al sector de la salud en departamentos de mantenimiento, ingeniería clínica o de servicios hospitalarios.

A Homogeneous Set-Theoretical Frame for Clustering Fuzzy Relational Data

Our purpose is to provide a set-theoretical frame to clustering fuzzy relational data basically based on cardinality of the fuzzy subsets that represent objects and their complementaries, without applying any crisp property. From this perspective we define a family of fuzzy similarity indexes which includes a set of fuzzy indexes introduced by Tolias et al, and we analyze under which conditions it is defined a fuzzy proximity relation. Following an original idea due to S. Miyamoto we evaluate the similarity between objects and features by means the same mathematical procedure. Joining these concepts and methods we establish an algorithm to clustering fuzzy relational data. Finally, we present an example to make clear all the process

Investment in preventing and preparing for biological emergencies and disasters: social and economic costs of disasters versus costs of surveillance and response preparedness

Biological emergencies such as the appearance of an exotic transboundary or emerging disease can become disasters. The question that faces Veterinary Services in developing countries is how to balance resources dedicated to active insurance measures, such as border control, surveillance, working with the governments of developing countries, and investing in improving veterinary knowledge and tools, with passive measures, such as contingency funds and vaccine banks. There is strong evidence that the animal health situation in developed countries has improved and is relatively stable. In addition, through trade with other countries, developing countries are becoming part of the international animal health system, the status of which is improving, though with occasional setbacks. However, despite these improvements, the risk of a possible biological disaster still remains, and has increased in recent times because of the threat of bioterrorism. This paper suggests that a model that combines decision tree analysis with epidemiology is required to identify critical points in food chains that should be strengthened to reduce the risk of emergencies and prevent emergencies from becoming disasters.

New scientific paradigms for probiotics and prebiotics

The inaugural meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) was held May 3 to May 5 2002 in London, Ontario, Canada. A group of 63 academic and industrial scientists from around the world convened to discuss current issues in the science of probiotics and prebiotics. ISAPP is a non-profit organization comprised of international scientists whose intent is to strongly support and improve the levels of scientific integrity and due diligence associated with the study, use, and application of probiotics and prebiotics. In addition, ISAPP values its role in facilitating communication with the public and healthcare providers and among scientists in related fields on all topics pertinent to probiotics and prebiotics. It is anticipated that such efforts will lead to development of approaches and products that are optimally designed for the improvement of human and animal health and well being. This article is a summary of the discussions, conclusions, and recommendations made by 8 working groups convened during the first ISAPP workshop focusing on the topics of: definitions, intestinal flora, extra-intestinal sites, immune function, intestinal disease, cancer, genetics and genomics, and second generation prebiotics. Humans have evolved in symbiosis with an estimated 1014 resident microorganisms. However, as medicine has widely defined and explored the perpetrators of disease, including those of microbial origin, it has paid relatively little attention to the microbial cells that constitute the most abundant life forms associated with our body. Microbial metabolism in humans and animals constitutes an intense biochemical activity in the body, with profound repercussions for health and disease. As understanding of the human genome constantly expands, an important opportunity will arise to better determine the relationship between microbial populations within the body and host factors (including gender, genetic background, and nutrition) and the concomitant implications for health and improved quality of life. Combined human and microbial genetic studies will determine how such interactions can affect human health and longevity, which communication systems are used, and how they can be influenced to benefit the host. Probiotics are defined as live microorganisms which, when administered in adequate amounts confer a health benefit on the host.1 The probiotic concept dates back over 100 years, but only in recent times have the scientific knowledge and tools become available to properly evaluate their effects on normal health and well being, and their potential in preventing and treating disease. A similar situation exists for prebiotics, defined by this group as non-digestible substances that provide a beneficial physiological effect on the host by selectively stimulating the favorable growth or activity of a limited number of indigenous bacteria. Prebiotics function complementary to, and possibly synergistically with, probiotics. Numerous studies are providing insights into the growth and metabolic influence of these microbial nutrients on health. Today, the science behind the function of probiotics and prebiotics still requires more stringent deciphering both scientifically and mechanistically. The explosion of publications and interest in probiotics and prebiotics has resulted in a body of collective research that points toward great promise. However, this research is spread among such a diversity of organisms, delivery vehicles (foods, pills, and supplements), and potential health targets such that general conclusions cannot easily be made. Nevertheless, this situation is rapidly changing on a number of important fronts. With progress over the past decade on the genetics of lactic acid bacteria and the recent, 2,3 and pending, 4 release of complete genome sequences for major probiotic species, the field is now armed with detailed information and sophisticated microbiological and bioinformatic tools. Similarly, advances in biotechnology could yield new probiotics and prebiotics designed for enhanced or expanded functionality. The incorporation of genetic tools within a multidisciplinary scientific platform is expected to reveal the contributions of commensals, probiotics, and prebiotics to general health and well being and explicitly identify the mechanisms and corresponding host responses that provide the basis for their positive roles and associated claims. In terms of human suffering, the need for effective new approaches to prevent and treat disease is paramount. The need exists not only to alleviate the significant mortality and morbidity caused by intestinal diseases worldwide (especially diarrheal diseases in children), but also for infections at non-intestinal sites. This is especially worthy of pursuit in developing nations where mortality is too often the outcome of food and water borne infection. Inasmuch as probiotics and prebiotics are able to influence the populations or activities of commensal microflora, there is evidence that they can also play a role in mitigating some diseases. 5,6 Preliminary support that probiotics and prebiotics may be useful as intervention in conditions including inflammatory bowel disease, irritable bowel syndrome, allergy, cancer (especially colorectal cancer of which 75% are associated with diet), vaginal and urinary tract infections in women, kidney stone disease, mineral absorption, and infections caused by Helicobacter pylori is emerging. Some metabolites of microbes in the gut may also impact systemic conditions ranging from coronary heart disease to cognitive function, suggesting the possibility that exogenously applied microbes in the form of probiotics, or alteration of gut microecology with prebiotics, may be useful interventions even in these apparently disparate conditions. Beyond these direct intervention targets, probiotic cultures can also serve in expanded roles as live vehicles to deliver biologic agents (vaccines, enzymes, and proteins) to targeted locations within the body. The economic impact of these disease conditions in terms of diagnosis, treatment, doctor and hospital visits, and time off work exceeds several hundred billion dollars. The quality of life impact is also of major concern. Probiotics and prebiotics offer plausible opportunities to reduce the morbidity associated with these conditions. The following addresses issues that emerged from 8 workshops (Definitions, Intestinal Flora, Extra-Intestinal Sites, Immune Function, Intestinal Disease, Cancer, Genomics, and Second Generation Prebiotics), reflecting the current scientific state of probiotics and prebiotics. This is not a comprehensive review, however the study emphasizes pivotal knowledge gaps, and recommendations are made as to the underlying scientific and multidisciplinary studies that will be required to advance our understanding of the roles and impact of prebiotics, probiotics, and the commensal microflora upon health and disease management.

Neural and wavelet network models for financial distress classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single "mother wavelet" function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997-2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.

Ratio selection for classification models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.

Subjectively construed identities and discourse: towards a research agenda for construction management

Identity issues are under-explored in construction management. We provide a brief introduction to the organization studies literature on subjectively construed identities, focusing on discourse, agency, relations of power and identity work. The construction management literature is investigated in order to examine identity concerns as they relate to construction managers centred on (1) professionalism; (2) ethics; (3) relational aspects of self-identity; (4) competence, knowledge and tools; and (5) national culture. Identity, we argue, is a key performance issue, and needs to be accounted for in explanations of the success and failure of projects. Our overriding concern is to raise identity issues in order to demonstrate their importance to researchers in construction management and to spark debate. The purpose of this work is not to provide answers or to propose prescriptive models, but to explore ideas, raise awareness and to generate questions for further programmatic research. To this end, we promote empirical work and theorizing by outlining elements of a research agenda which argues that 'identity' is a potentially generative theme for scholars in construction management.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.