102,903 research outputs found

    An initial state of design and development of intelligent knowledge discovery system for stock exchange database

    Get PDF
    Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

    Transforming Graph Representations for Statistical Relational Learning

    Full text link
    Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

    Creativity and the Brain

    Get PDF
    Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

    Automatic annotation of bioinformatics workflows with biomedical ontologies

    Full text link
    Legacy scientific workflows, and the services within them, often present scarce and unstructured (i.e. textual) descriptions. This makes it difficult to find, share and reuse them, thus dramatically reducing their value to the community. This paper presents an approach to annotating workflows and their subcomponents with ontology terms, in an attempt to describe these artifacts in a structured way. Despite a dearth of even textual descriptions, we automatically annotated 530 myExperiment bioinformatics-related workflows, including more than 2600 workflow-associated services, with relevant ontological terms. Quantitative evaluation of the Information Content of these terms suggests that, in cases where annotation was possible at all, the annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014 conference), 15 pages, 4 figure

    Toward Self-Organising Service Communities

    Get PDF
    This paper discusses a framework in which catalog service communities are built, linked for interaction, and constantly monitored and adapted over time. A catalog service community (represented as a peer node in a peer-to-peer network) in our system can be viewed as domain specific data integration mediators representing the domain knowledge and the registry information. The query routing among communities is performed to identify a set of data sources that are relevant to answering a given query. The system monitors the interactions between the communities to discover patterns that may lead to restructuring of the network (e.g., irrelevant peers removed, new relationships created, etc.)

    National Systems of Innovation and Entrepreneurship: In Search of a Missing Link

    Get PDF
    The literature on national systems of innovation (NIS) has neglected the issue of entrepreneurship because of several incompatibilities between the two notions. The Schumpeterian legacy, the current person-centric view of entrepreneurship, and methodological problems related to treating entrepreneurship at the macro-level, have made it difficult to integrate entrepreneurship into the NIS perspective. At national level it is more appropriate to treat entrepreneurship as a 'property' (dimension) of NIS. In order to link NIS and entrepreneurship we must establish a common conceptual basis. Our argument is that the functional view of NIS and entrepreneurship presents a common basis for such an approach. We develop criteria for the entrepreneurial NIS which we define as being those that can change balance between individual and cooperative entrepreneurship; that enhance both the opportunity and skill aspects of entrepreneurship; and that can balance generation of uncertainty with support to business models and other organisations which pool uncertainty. From the NIS perspective, we explain entrepreneurship as a systemic phenomenon driven by complementarities between technological, market and institutional opportunities. This framework builds on three research traditions in the entrepreneurship/NIS literature (Schumpeterian, Kirznerian and Listian) which jointly form a multi-level, multi-dimensional framework for understanding entrepreneurship from a NIS perspective. This framework could be useful as a heuristic for empirical research on entrepreneurship. Finally, we analyse policies for entrepreneurship and find that they are highly dependent on underlying and previously discussed conceptions of entrepreneurship

    Survey of data mining approaches to user modeling for adaptive hypermedia

    Get PDF
    The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
    corecore